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

No annotations are provided, so the description carries the burden. It usefully discloses that the result is 'an answer plus the source URLs it was drawn from', which is genuine behavioral context. It says nothing about latency, freshness, or whether answers are authoritative, so for an unannotated tool this is only mid-range.

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?

Two tight clauses: what to ask, then what comes back. Zero waste and the purpose is front-loaded.

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 single-parameter read tool there is no output schema, and the description compensates by stating the return shape (answer + source URLs). The only gap is the absence of any routing guidance against the two sibling tools.

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% and the schema already supplies a concrete example ('How much does Cody cost?'). The description adds no syntax or format guidance beyond that, so the baseline 3 for a fully documented single parameter applies.

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?

States a specific verb+resource ('Ask Cody a question about the product') and scopes the domain to pricing, setup, integrations, MCP support and data handling, which reads clearly against the sibling set. It stops short of explicitly naming how it differs from search_team_knowledge, so it is clear but not fully sibling-differentiated.

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?

Usage is implied by the topic list (product questions), but there is no explicit statement of when to use this versus search_team_knowledge or list_integrations, and no exclusions. Adequate but the agent must infer the routing.

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

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

No annotations are provided, so the description carries the full disclosure burden. It usefully names the return payload (integration ids, categories, documentation links), which implies a safe read-only listing, but says nothing about pagination, result limits, or whether documentation links can be null when absent.

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?

Two short sentences, zero filler, with the core purpose front-loaded and the return contents following. Every clause earns its place.

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 two-optional-parameter read-only list tool with no output schema, the description covers purpose, filtering, and the shape of the returned data, which is enough for correct invocation. Minor gaps around pagination and empty-result behavior keep it from being fully complete.

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 parameters are already fully documented in the schema, including the case-insensitive substring semantics for 'search' and example categories for 'category'. The description only restates the filter dimensions at a high level, adding no syntax or matching details beyond the 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?

States a specific verb (list) and resource (integrations that Cody connects to), plus the optional filter dimensions. The sibling tools (search_team_knowledge, ask_cody) are clearly distinct operations, so no explicit differentiation is needed for an agent to route correctly.

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 makes clear the tool is used to enumerate integrations, with filtering framed as optional ('optionally filtered by category or name'), which sets expectations about default behavior. It stops short of any explicit when-not-to-use condition, but no sibling competes for this job.

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

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

With no annotations, the description carries full burden; it does disclose the return shape (ranked articles with titles, summaries, canonical URLs on agentcody.ai), which substitutes for a missing output schema. It says nothing about authentication, rate limits, or what happens with zero results, so behavioral disclosure is partial.

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?

Two tight sentences with the purpose front-loaded and the return format following. Slightly redundant in restating 'articles' across both sentences, but no wasted verbiage.

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 two-parameter read tool with fully documented schema and an explicit statement of returned fields, the description is nearly self-sufficient. Only the relationship to the sibling tools is left unaddressed.

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 query and limit are already documented with examples and defaults. The description's only added semantic is that results are 'ranked', which implies relevance ordering but no ranking criteria — 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 description gives a specific verb (Search) and a well-defined resource (Cody's team knowledge base articles on setup, integrations, support automation and MCP). It clearly scopes the corpus, though it does not explicitly distinguish this from the sibling ask_cody, which could overlap for question-answering.

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?

There is no explicit when-to-use guidance, no 'when not to use', and no named alternative such as ask_cody or list_integrations. The topical coverage hints at the intended domain, but the agent must infer when a search is preferable to asking Cody directly.

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