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

No annotations are provided, so the description carries the full burden. It does disclose a key behavioral trait: the content is 'untrimmed' and includes 'every comment,' which hints at completeness and potential payload size. However, it does not explicitly state that the tool is read-only, nor does it mention authentication, rate limits, or lack of side effects. The phrase 'when a thread needs to be read' implies read-only, but this is not a direct disclosure.

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 sentences with no filler. The first sentence front-loads the core behavior (full untrimmed issue text), and the second provides actionable usage context. Every word 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 simple read tool with two parameters and no output schema, the description is nearly complete: it states the return content, the usage context, and the schema fully documents the parameters. Minor gaps are the lack of explicit error conditions and exact output structure, but these are unlikely to mislead an agent deciding to call this 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?

Schema description coverage is 100% — both `repo` and `number` are already described in the input schema with clear formats ('owner/name' and 'Issue number'). The description adds no additional parameter-level meaning, so it correctly sits at the baseline for full schema coverage.

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 states exactly what the tool returns: 'The full text of one issue: body and every comment, untrimmed.' This clearly identifies the resource and scope, and distinguishes it from siblings like `triage` and `search_issues`, which are presumably summary/list tools. An agent can tell this tool apart without opening the schema.

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 explicitly tells the agent when to use it: 'Use after `triage` or `search_issues` when a thread needs to be read or summarised.' This gives a clear workflow context and names the alternatives, satisfying the when/when-not guidance with no inference needed.

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 burden of behavioral disclosure. It explains the bidirectional nature of the cross-references ('from an issue, and ones that reference it back'), which is useful. However, it does not disclose return format, pagination, or whether the result is a list vs. a single object, leaving some behavioral ambiguity.

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 sentences with no filler. The core behavior is front-loaded, and the use cases are given in the second sentence. Every word 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 simple read-style tool with two fully documented parameters and no output schema, the description covers the essential context: what the tool returns and why an agent would use it. It could mention that the result is a list or that it may be empty, but that is a minor gap given the tool's simplicity.

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 the schema already documents both parameters (repo as 'owner/name' and number as issue number). The description adds the context that 'number' is the issue whose linked items are being retrieved, but it does not add meaning beyond the schema's own descriptions. 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 states a specific verb ('find') and resource ('issues and pull requests cross-referenced from an issue, and ones that reference it back'), and it names two concrete use cases: finding the PR that implements an issue, or the sibling issue a discussion moved to. This clearly distinguishes it from siblings like get_issue (single issue) and search_issues (query-based search).

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 explicit use cases ('Use to find the PR that implements an issue, or the sibling issue a discussion moved to'), which tells an agent when to invoke it. It does not explicitly state when not to use it or name alternatives, but the use cases are specific enough to route correctly against the sibling tools.

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 burden. It discloses scope handling but omits return format, pagination, authentication needs, or rate limits—important behavioral context for a search tool.

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?

A single well-formed sentence that front-loads the purpose and adds a crucial scope caveat. 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?

The description is too sparse for a tool with no output schema and no annotations. It does not explain what results look like, how defaults apply, or provide sufficient usage context for an agent to call it correctly beyond basic syntax.

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 coverage is 100%, so the baseline is 3. The description adds meaningful parameter context: it explains that 'repos' overrides the configured scope, and 'GitHub search syntax' enriches the query parameter beyond schema examples.

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 searches issues using GitHub search syntax, giving a specific verb and resource. It does not explicitly contrast with siblings like get_issue or linked_issues, but the search intent is evident and distinct enough.

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 given on when to use this tool versus alternatives. The note about automatic scope addition explains behavior but does not help an agent decide between search_issues, get_issue, or linked_issues.

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 the behavioral burden: it discloses the Markdown report format, instructs the agent to relay it verbatim instead of rewriting, and reveals the sorting behavior through the four categories. It does not state whether the operation is read-only or whether it uses authenticated user context, but the word 'open issues that involve you' strongly implies a personal read-scoped query.

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?

Three sentences, each earning its place: first defines the resource and sorting, second tells the agent how to handle the output, and third gives the entry-point directive. No wasted filler or repeated schema content.

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 no-required-parameter report tool, this is complete: it tells the agent what scope it covers, how results are organized, what the output format is, and what to do with that output. Parameter details are handled by the schema, so nothing essential is missing for correct invocation.

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 already describes both parameters fully: limit with a default of 60 and repos as an override of the configured scope. The description adds no semantics beyond the schema, so the baseline of 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 names a distinct resource ('Open issues that involve you') and a specific presentation ('sorted by whose turn it is'), so a reader knows exactly what the tool returns. It does not explicitly contrast with siblings like search_issues or get_issue, but the personal-scope and turn-taking organization make the difference clear without that contrast.

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?

'Start here when asked what needs attention' is an explicit when-to-use signal. The description also clarifies scope ('issues that involve you') and how to handle output, but it does not state when not to use it or name alternative tools for other cases.

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