should_i_useHonest verdict on a specific library: 4-axis scores with reasons, when to skip it, and maintenance signals (last push, weekly downloads).
| Name | Required | Description | Default |
|---|---|---|---|
| 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 indicate read-only and non-destructive behavior. The description adds meaningful context beyond annotations by detailing the output composition: '4-axis scores with reasons, when to skip it, and maintenance signals (last push, weekly downloads).' This gives the agent a clear expectation of the tool's response shape and evaluative tone, without contradicting the annotations.
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 a single, well-structured sentence that leads with the core purpose ('Honest verdict on a specific library') and concisely lists the key outputs. Every word earns its place, with no redundancy or filler.
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?
Given the tool's simplicity (one parameter) and the presence of an output schema, the description provides a sufficient high-level summary of what the tool returns. It mentions the main components (scores, reasons, skip advice, maintenance signals) without needing to explain return format in detail, as the output schema likely handles that. It is complete for the tool's context, though it could mention that the verdict is based on these axes.
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?
The input schema already provides 100% coverage with a clear description of the 'library' parameter as 'Library name as published on npm or PyPI'. The description does not add any additional semantics beyond calling it 'specific library'. Since schema coverage is high, the baseline is 3; no extra value is added.
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's purpose with a specific verb and resource: it provides an 'honest verdict on a specific library' with detailed components (4-axis scores, reasons, skip advice, maintenance signals). This distinguishes it from sibling tools like 'pick_library' or 'alternatives' which likely involve comparisons or selections.
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 implies usage when evaluating a specific library, but it does not explicitly state when to use this tool versus alternatives or provide exclusions. Sibling tools like 'alternatives' or 'pick_library' might be used for different tasks, but no guidance is given. The phrase 'on a specific library' hints at scope but lacks explicit direction.
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.