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Proxima presents 19 tools with consistent naming (verb_noun pattern: ask_*, generate_*, verify_*, etc.) and descriptions in the 40-150 character range. However, critical structural issues prevent a higher score: (1) Input schemas are visible but lack EXPLICIT type constraints in the provided source, parameters are described as arrays or strings but no JSON Schema with type/format/enum declarations are shown. (2) No output schemas are documented anywhere; LLMs cannot predict what these tools return, making chaining and result parsing unreliable. (3) Error handling is absent, no guidance on retryability, failure modes, or recovery steps. (4) Descriptions, while present, are weak on WHEN to use each tool vs. similar ones; ask_chatgpt, ask_claude, ask_gemini, ask_perplexity are nearly identical in structure, creating disambiguation ambiguity. (5) Several tools have subtle responsibility overlap (ask_all_ais vs. smart_query with 'verify' mode; verify_code vs. review_code; content tool with 'analyze' action vs. explain_code). Baselines: average param count is ~3, descriptions average ~110 chars (within acceptable 10-1024 range), but 0% of tools show documented return types, violating critical checks for output schemas and composition patterns.
Send the SAME message to multiple providers in parallel and get every answer side by side. Use for breadth/comparison; for a single best answer use smart_query.
Send a message to ChatGPT specifically. Use ask_model for any other/BYOK provider, smart_query to auto-pick the best provider, or ask_all_ais to query several at once.
Send a message to Claude specifically (strong at coding/reasoning). Use ask_model for other/BYOK providers, smart_query to auto-pick, or ask_all_ais for several at once.
Send a message to Gemini specifically. Use ask_model for other/BYOK providers, smart_query to auto-pick, or ask_all_ais for several at once.
Universal chat: send a message to ANY enabled provider by name (the 4 session providers OR any configured BYOK provider). Use this when you need a provider other than the four dedicated ask_* tools.
No output schemas documented for any tool. LLMs cannot determine what fields/types to expect, preventing reliable result chaining and downstream tool integration.
Input parameter schemas lack explicit JSON Schema type/format/enum declarations in source. Parameters are described informally (e.g., 'array', 'string') but no schema validation structures visible. Cannot verify parameter constraints are machine-enforced.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | D | 50 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 20 | - | v1 |
Send a message to Perplexity specifically (best for web search + citations). Use ask_model for other/BYOK providers, or deep_search for structured research.
Design a complete, production-ready system architecture (stack, schema, APIs, deployment) from a project description.
Compare two items/options/technologies side by side, optionally for a given context.
General text/content tool: summarize, write, brainstorm, howto, analyze, extract, or improve. For code use the code tools; for research with sources use deep_search.
Have multiple providers argue DIFFERENT stances on a topic (one stance each), then conclude. Needs 2+ enabled providers for true multi-AI; otherwise one AI covers all sides.
Explain a code snippet (or files) in detail, line by line. Use this to understand existing code; use analyze_file for large files/whole codebases.
Diagnose a specific error message/stack trace and give the exact fix (root cause → fix → prevention → full code). For a general explanation only, use explain_error.
Write new, production-ready code from a description (with comments, error handling, usage examples). For converting existing code use convert_code; for fixing an error use fix_error.
Improve existing code for a goal (speed, memory, readability) and show before/after. For correctness bugs use fix_error; for security use security_audit.
Review a code snippet for bugs, security, performance and best practices. For a single file on disk use review_code_file; for a folder use analyze_file.
Best general entry point: auto-routes to the best provider and can verify/cross-check. Modes: auto (fast single AI), verify (primary + cross-model check), research (web search), or debug (error fixing).
End-to-end solve a coding task/feature/bug with full working code. Broadest tool: use generate_code for greenfield code, fix_error for a specific error message.
Ask one or more providers to answer a question/claim with a confidence rating and caveats. Use to fact-check or cross-check an answer across providers.
Quick best-practices check of a code snippet for a stated purpose (bugs, security, improvements). For a whole file/folder use analyze_file; for a deep vulnerability audit use security_audit.
Four nearly-identical ask_* tools (ask_chatgpt, ask_claude, ask_gemini, ask_perplexity) create LLM disambiguation ambiguity. Descriptions say 'Use this for X provider' but do not explain when to prefer direct provider tools vs. smart_query or ask_model. Composition pattern violated.
No error handling guidance. Tools have no documented error responses, retryability, or recovery paths. LLMs cannot learn from failures or attempt self-correction.
Tool 'content' uses a generic action enum (summarize, write, brainstorm, howto, analyze, extract, improve) bundling 6 distinct concerns into one tool. Should split into separate tools (summarize_text, write_article, brainstorm_ideas, analyze_document, extract_data, improve_writing) for clarity and single responsibility.
Overlapping tool responsibilities: verify_code vs. review_code (both check code quality), explain_code vs. solve (both can work with code), smart_query+verify vs. ask_all_ais (both compare multiple providers). Descriptions do not clarify which to choose.
No pagination support documented for tools that could return large result sets (ask_all_ais, debate with multiple providers, content actions like summarize). Large results risk exceeding context limits without limit/offset/cursor parameters.
Tools that reference external files (ask_* with files param, explain_code with files, etc.) do not specify file size limits, supported formats, or handling of binary files. Agents may attempt to pass gigabytes or unsupported formats without guidance.
Parameters with implicit enums not formalized: mode in smart_query (auto|verify|research|debug), action in content (summarize|write|brainstorm|howto|analyze|extract|improve) lack enum schema constraints. LLMs may hallucinate invalid values.