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Exposes Agent Prime's PM skill library to other AI agents. Provides access to 12 product management frameworks covering competitive analysis, discovery research, problem framing, specifications, metrics, narrative building, strategy, GTM, pricing, executive writing, publishing, and stakeholder alignment.
PM Skills Arsenal MCP Server exhibits significant definition quality gaps across all dimensions. While tool naming follows verb-noun conventions, descriptions are inconsistent in depth and technical precision. Input schemas are visible but lack comprehensive parameter descriptions and type constraints. The server provides READ_ONLY tools for skill discovery and execution prep, but output schemas are not formally documented. Error handling is absent, no recovery guidance or error categorization visible. The implementation appears to be proof-of-concept rather than production-grade.
Get benchmark results for a skill. Returns benchmark score and details if available.
Load a complete skill file by name. Returns the full SKILL.md content as a string. If the skill is not found, returns an error message.
List Agent Prime's agent capabilities. Returns JSON array of agent objects with name, capability, description.
List all available PM skills with metadata
Prepare skill + context for LLM execution
Missing output schema documentation. Tools like list_skills, get_skill, list_agents, get_benchmark, and run_skill have no documented return types or structured field descriptions. LLMs cannot infer response structure without explicit documentation, forcing them to guess at available fields and types.
Input parameter descriptions missing or incomplete. The get_skill tool accepts 'skill_name' with only a brief description. The run_skill tool has 'skill_name' and 'context' but provides no guidance on format, length constraints, or valid skill names. Parameters lack type constraints like enums or patterns.
No error handling or recovery guidance. Tools have no documented error cases, retryable vs. fatal classifications, or actionable error messages. If a skill is not found or context is invalid, the LLM has no guidance on what to do next.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 49 | 2026-07-28+ | v2 |
Tool descriptions are under-optimized for LLM selection. Descriptions like 'Prepare skill + context for LLM execution' (run_skill) are vague and fail to explain WHEN to use this tool vs. alternatives, WHAT it returns, or HOW it differs from get_skill.
No pagination support documented. Tools like list_skills and list_agents return arrays with no limit, offset, or pagination parameters visible.
Ambiguous tool differentiation. The purpose of run_skill vs. get_skill is not clearly distinguished. Both deal with skills; one appears to load content, the other to 'prepare' for execution. The semantic boundary is unclear, forcing LLMs to guess which to call first.
No constraint documentation for skill_name parameters. The SKILL_CATALOG is hardcoded in the server, but tool descriptions do not enumerate valid skill names, reference the catalog, or provide examples. LLMs must guess which names are valid, risking failed calls.