docs_linkOfficial docs URL and GitHub repo for a library, one line each. Use when the clamped answers are not enough.
| Name | Required | Description | Default |
|---|---|---|---|
| topic | No | Optional topic to look up in the docs | |
| library | Yes | Library name as published on npm or PyPI |
| Name | Required | Description | Default |
|---|---|---|---|
| answer | Yes | The clamped text answer (hard limit ~500 tokens) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds behavioral context by specifying the output format ('one line each') and that sources are 'official.' It does not disclose potential failure modes or edge cases, but given the strong annotation coverage, this is acceptable.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long and front-loaded with the tool's core purpose. Every word earns its place, and there is no fluff or repetition of schema information. It is highly concise and structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple lookup tool with an output schema present, the description covers the essential aspects: what it returns (docs URL and GitHub repo), the format (one line each), and when to use it. It lacks details about error handling for unknown libraries, but the output schema likely documents return structure. Overall, it is sufficiently complete for an AI agent to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both 'library' and 'topic' having descriptions in the schema. The tool description does not add extra parameter-level details beyond the schema, so the baseline score of 3 is appropriate. It does re-emphasize 'library' implicitly, but no additional semantics are provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns 'Official docs URL and GitHub repo for a library,' using a specific verb (returns) and resource (library docs/repo). This distinguishes it from sibling tools like pick_library or should_i_use, which focus on selection or evaluation rather than providing links.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes an explicit usage condition: 'Use when the clamped answers are not enough.' This provides clear context for when to invoke the tool, though it does not explicitly name alternative tools or state when not to use it. The sibling list is available for context, so the guidance is adequate but not exhaustive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct phase: deciding on a library (should_i_use, pick_library, alternatives), learning usage (how_do_i, docs_link), and auditing existing dependencies (audit_dependencies). There is no meaningful overlap between any two tools, as they clearly differentiate by input (task vs. library) and output (recommendation vs. snippet vs. docs).
The names are all snake_case but mix imperative verbs (audit_dependencies, pick_library), nouns (alternatives, docs_link), and question phrases (how_do_i, should_i_use). This makes the set readable but not predictably patterned; an agent cannot anticipate a consistent verb_noun structure.
Six tools is well-scoped for a library advisory server, covering selection, verification, documentation, usage, and dependency auditing without redundancy. The count is within the ideal range and each tool serves a distinct purpose.
The tool set covers the full lifecycle: pick a library (pick_library), evaluate it (should_i_use), see alternatives (alternatives), get usage snippets (how_do_i), get official docs (docs_link), and audit a project's dependencies (audit_dependencies). There are no obvious dead ends for common library decision workflows.