Fetch a reproducible method: reading a browser extension's permissions, measuring OCR recall with a control run, or choosing between print-to-PDF and screen capture. Omit the argument to list them.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description is the sole source of behavioral disclosure. It does reveal that omitting the argument lists available methods, but it doesn't state whether the operation is read-only, if it has side effects, or what happens for invalid names. This falls short of full transparency.
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 with a clear colon-separated enumeration of examples, followed by a direct instruction about the optional argument. Every clause adds value; there's no filler or redundancy.
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 optional param, no output schema), the description covers the core purpose, example methods, and argument behavior. However, it doesn't describe the return format or error behavior, which would be helpful for agents unfamiliar with the tool.
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 schema provides a single optional string parameter 'name' with no description. The description clarifies the argument's role: providing a name fetches a specific method, and omitting it lists all methods. It also gives concrete examples of method topics, giving the agent a sense of valid values, though it doesn't specify exact string formats.
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 fetches a reproducible method for specific tasks, citing examples like browser extension permissions, OCR recall, and print-to-PDF vs screen capture. The verb 'Fetch' and resource 'method' make the purpose unambiguous, though it doesn't explicitly distinguish from similar sibling tools like 'how_to_capture'.
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 through examples ('reading...', 'measuring...', 'choosing between...'), suggesting the tool provides procedural guidance for these scenarios. However, it lacks explicit 'when not to use' guidance or references to alternative sibling tools, so the context is implied rather than explicit.
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.
Most tools have clearly distinct purposes, such as single vs batch citation extraction. The only potential confusion is between list_measurements and get_measurement_data, both of which return measurement data; however, their descriptions indicate one lists available data and the other retrieves a specific figure's backing data.
Tool names predominantly follow a verb_noun pattern (extract_citation, install_extension, list_measurements). Two exceptions—adoption_stats and how_to_capture—deviate from this pattern, but they are still readable and not chaotic.
Ten tools is a reasonable scope for a specialized server covering citation extraction, measurements, capture guidance, and project status. No tool is redundant, and the count is neither too sparse nor overwhelming.
The server covers the main workflows: single/batch citation extraction, retrieving and listing measurement data, fetching reproducible methods, and obtaining capture setup instructions. Minor gaps exist, such as no tool for creating new measurements, but the domain is primarily read-only and contribution is handled through open_work.
