Retrieve an evidence-first map of a stored paper's theorem dependencies, giving a structured overview on first load to understand how theorems connect.
Return an evidence-first first-load map for one stored paper.
| Name | Required | Description | Default |
|---|---|---|---|
| paper_id | Yes | ||
| max_candidates | No |
Changes observed during successful MCP inspections.
v0.10.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must fully disclose behavior. It indicates a read-like operation ('Return') but does not state whether it is read-only, what happens on missing paper_id, whether max_candidates affects results or errors, or any side effects. The 'evidence-first first-load map' is left undefined, so the agent cannot anticipate the output shape or failure modes.
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, tightly worded sentence with no filler or repetition. It front-loads the core purpose and avoids redundancy. However, its brevity borders on under-specification, which is why it earns a 4 rather than 5 – it is concise but sacrifices essential detail.
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?
The tool has two parameters, no output schema, and no annotations, so the description is the sole source of context. It fails to explain what a 'map' is, what 'evidence-first' means, how max_candidates behaves, or what the return value looks like. For a tool with this complexity, the description is clearly insufficient for an agent to invoke it 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 0%, so the description must explain both parameters. It mentions neither paper_id nor max_candidates. paper_id is self-explanatory, but max_candidates is cryptic; the agent has no idea what it limits (candidates for what?) or how it influences the result. The description adds zero value over the raw schema.
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 it returns a specific artifact ('evidence-first first-load map') for a single stored paper, using a distinct verb ('Return'). It distinguishes itself from sibling tools like workspace_get_paper (which likely returns the paper object) and workspace_get_evidence (which returns evidence). However, the term 'evidence-first first-load map' is jargon and not explained, which slightly reduces clarity.
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?
No guidance is provided on when to use this tool versus alternatives. There is no mention of when it is appropriate, what scenarios it fits, or when to choose a sibling tool instead. The description is purely declarative with no usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.