ashby_get_pipeline_summary

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

In addition to the readOnlyHint annotation, the description discloses that the server fetches and aggregates all applications and removes pagination overhead. It also previews response structure. No contradiction with annotations was found.

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 compact and well organized: a declarative summary, usage examples, an aggregation note, and a response shape. Every sentence earns its place and the most important information comes first.

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?

With only two optional parameters fully documented in the schema, readOnlyHint present, and no output schema, the description still provides the return shape and explains aggregation/pagination behavior. This is sufficient for an agent to call 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 100%, so both job_id and status already have clear descriptions, defaults, and enum. The tool description does not add parameter-level meaning beyond that, so a baseline 3 is appropriate.

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 opening sentence names a specific verb and resource: get a pipeline summary with candidate counts per interview stage per job. The example questions ('What does our pipeline look like?') further anchor its purpose and distinguish it from raw list tools like ashby_list_applications.

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 gives concrete trigger questions and explicitly notes that the MCP server aggregates internally so no manual pagination is needed. It does not state exclusions or name sibling alternatives, but the intended use cases are clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.