The TX MCP server exposes 55 tools across task management, learning, sync, messaging, documentation, and auxiliary domains. However, WITHOUT ACCESS TO THE ACTUAL SOURCE CODE IMPLEMENTATION (only tool names and brief descriptions are provided), I cannot verify: (1) whether input parameter schemas exist and are properly typed, (2) whether parameter descriptions are present, (3) what output schemas are documented, (4) whether error handling provides recovery guidance, or (5) actual error categorization. The provided source excerpt shows only package.json files and does not include the tool definition code itself (e.g., apps/cli/src/mcp/tools/task.ts, apps/cli/src/mcp/tools/learning.ts, etc.). The aggregate across 55 tools at max 50 each, combined with the inability to verify schemas, parameter descriptions, and output documentation, yields a low overall score. The tool portfolio is ambitious (55 tools across many domains) and naming follows verb_noun convention reasonably well (create_task, list_tasks, update_task, etc.), which earns baseline points. However, without verifiable schema evidence, this server cannot achieve a passing score on Definition Quality.
NO VERIFIABLE INPUT SCHEMAS. Source code provided shows only package.json files; actual tool implementations (e.g., apps/cli/src/mcp/tools/task.ts) are not included. Cannot verify that input parameters have types, constraints, or descriptions.
URGENT: Provide the complete source code for all tool implementations (apps/cli/src/mcp/tools/*.ts) so that input schemas, parameter descriptions, output types, and error handling can be verified. Current evaluation is based on inferred tool names only.
Expand all tool descriptions from ~20 chars to 100 - 200 chars. Include: what the tool does, when to call it (vs. similar tools), what it returns, and any prerequisites. Example: 'Mark a task as complete. Call after all subtasks are done (use get_ready_tasks to find blockers). Returns updated task with completion_time. Irreversible, agents should confirm before calling.'
Define explicit JSON Schema for all tool inputs. Every parameter must have: name, type (string|number|boolean|array|object), description (50+ chars), and constraints (enum, minLength, maxLength, pattern, min, max, items for arrays). Example: { 'task_id': { 'type': 'string', 'description': 'UUID of the task to mark complete. Obtainable from create_task or list_tasks.', 'pattern': '^[0-9a-f-]{36}$' } }
Define and document output schemas for all tools. List tools must specify: results (array of items), total_count (integer), has_more (boolean), next_cursor (string, optional). Create/Update tools must return full object with all IDs needed by downstream tools.
Clarify domain boundaries. Decide: is 'learning' a short-term fact/insight, while 'memory_document' is a persistent knowledge base? Is 'pin' the same as a bookmarked learning? Document the semantic distinction or merge redundant tools.
TRIVIAL DESCRIPTIONS (under 40 chars). Tool descriptions such as 'Create a new task', 'List tasks with optional filtering', 'Get a specific task by ID', 'Update an existing task' are minimal and do not explain WHEN to use them, their side effects, prerequisites, or how they relate to similar tools. No guidance on dependencies, postconditions, or recovery steps.
VAGUE TOOL NAMES ('verify', 'reflect'). These names do not clearly signal what action is taken. 'verify' could mean validation, verification of signatures, or assertion checking. 'reflect' is abstract, does it analyze, summarize, or generate insights?
NO VERIFIABLE OUTPUT SCHEMAS. Cannot confirm that list_* tools return pagination (limit, offset, total_count) or that create_* tools return IDs needed by downstream tools. Per pattern: 'Document the output schema. LLMs need to know what fields to expect so they can plan downstream tool calls.' Without visible schemas, chaining risk is high.
AMBIGUOUS DOMAIN OVERLAP. Multiple tools with similar intent (e.g., create_learning + create_file_learning, create_memory_document + create_learning, list_learnings + list_memory_documents). Descriptions do not clarify when to use one vs. another.
NO VISIBLE ERROR HANDLING GUIDANCE. Cannot verify that error responses include recovery instructions, categorization (retryable vs. user-fixable), or actionable next steps. Per pattern: 'Error responses must tell the LLM what to do next.' Without evidence, assume none.
NO VISIBLE PARAMETER DESCRIPTIONS. Source code does not show parameter objects with type and description fields. Cannot verify constraints (enums, min/max, regex patterns, length limits).
Add error response specifications. For each tool that writes (create_*, update_*, delete_*, send_*, mark_*), document: (a) what errors are possible, (b) whether they are retryable, (c) what the LLM should do (e.g., 'Task not found. Call list_tasks to search, then retry.').
For list_* tools, add pagination: limit (default 20, max 100), offset (default 0), and return total_count. Prevent context window explosion.
Add per-parameter examples in schema (not description text). Schema format allows 'example' fields, use them instead of example values in descriptions. This prevents LLMs from treating examples as the only valid input.
For write operations (create_task, send_message, sync_learnings), document idempotency. State whether repeated calls with the same input produce the same result or risk duplicates. If non-idempotent, suggest a confirmation tool (e.g., confirm_send_message).
Add tool annotations (readOnlyHint, destructiveHint, idempotentHint) to categorize risk. Example: 'send_message' should have destructiveHint=true (irreversible to user). 'list_tasks' should have readOnlyHint=true.
Document what IDs/references each tool returns and which tools accept them as input. Build a reference chain: create_task returns task_id → update_task accepts task_id → mark_task_done accepts task_id. Verify no broken links.
For tools that filter or search (search_learnings, search_memory, list_tasks with filters), document the query syntax and search scope. Example: 'search_learnings accepts a free-text query. Searches across title, content, and tags. Returns up to 20 most relevant results, sorted by relevance.'
Add guard tools where needed. For irreversible operations (delete, send, mark_done on critical tasks), introduce a pre-flight check or confirmation tool that returns a summary for the LLM to approve before proceeding.
Provide a tool relationship diagram or table showing: (a) which tools produce output used by other tools, (b) prerequisite relationships (must call X before Y), (c) mutually exclusive tools. This guides agent planning and prevents dead-end sequences.