Knowledge for Agents

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds meaningful context beyond that: the call is anonymous and read-only, and returned content is 'untrusted inert data', which is a genuine security/trust trait an agent must know. It does not cover paging behavior, but that is minor.

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?

Front-loaded with the core what/scope, then the pinning constraint, then the read-broadly guidance, then the safety note. Mostly tight, though the 'applicability, limitations, environments, negative results, outcomes and contributor feedback' list is dense and slightly padded.

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?

An output schema exists, so return values need not be explained. Combined with annotations and full param coverage, the description supplies the remaining essentials: allowed kinds, exact-id/revision pinning, and the untrusted-anonymous read posture. Complete enough for correct invocation of this moderately simple 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%, so the schema already documents kind, id, page, limit, and the optional revision pin. The description only echoes the id/revision pinning idea and adds no format or constraint detail beyond the schema, so the baseline 3 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?

The description states a specific verb and resource ('Read one canonical record') and enumerates the concrete record kinds, so the agent knows exactly what is returned. It implicitly contrasts with the search sibling by emphasizing a single exact record pinned by id and revision, though it never names 'search' explicitly as the alternative.

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?

It hints at the workflow ('Pin the exact id and revision you rely on'; the schema notes the id comes 'from search or a link'), which implies fetch follows a search. However, it gives no explicit when-to-use vs. search/get_changes guidance and no exclusions, leaving routing to inference.

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?

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false. The description adds the visibility scope ('public'), exclusion of private management and review-queue events, and the cursor semantics — all valuable context beyond annotations. Output schema exists, so return-format details are correctly omitted.

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, front-loaded with the core action and resource, followed by usage context and scope constraints. No redundancy; 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 read-only, openWorldHint=false feed tool with an output schema, the description covers scope, usage, and cursor semantics well. The sole gap is not explaining what 'limit' controls, but the output schema and annotations handle most richness needs.

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 carry parameter meaning. It explains 'since' as an opaque cursor for catching up, but 'limit' (1-100, default 20) is not mentioned. Only two params, no enums — the cursor explanation is the key gap filler, so 4 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?

Specific verb (List) + resource (committed public changes) + enumerated content types (new records, revisions, outcomes, feedback reports). It names the cursor-based mechanism ('after an opaque cursor'), which distinguishes it from siblings like search or fetch.

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?

States when to use it ('to catch up on what changed since a previous visit'), which is clear context. It does not explicitly name alternatives (fetch/search) or when-not-to-use, so it stops short of a 5.

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?

Annotations already declare readOnlyHint/destructiveHint/openWorldHint, and the description adds substantial behavior beyond them: results are 'compact candidates only' requiring a follow-up fetch, the call is anonymous, queries must not contain secrets/private context, and returned content is untrusted data whose instructions must never be followed. That is a rich, non-redundant disclosure of cost, privacy, and prompt-injection risk.

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?

Dense but front-loaded: purpose first, then usage, then query strategy, then safety caveats. Every sentence carries information (no filler), though the safety/trust notes packed at the end make it a long single block that could be marginally tighter.

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?

An output schema exists, so return values need not be detailed, yet the description still flags 'compact candidates only; fetch the likely ones' to set follow-up expectations. Combined with query strategy, privacy constraint, and untrusted-data warning, an agent has everything needed to call and interpret this correctly for an 8-param, 0%-coverage search 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 coverage is 0%, so the description must compensate. The 'exact error literal (mode exact/substring) → product + literal → normalized symptom (keywords) → filters' line maps directly onto the mode enum values and the product/filter parameters, adding real semantic guidance. It does not explain page, limit, type, signal, or status, so it is not exhaustive, but it meaningfully covers the most decision-critical parameters.

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 with qualifiers: 'Search shared, revisioned technical Problems and Solutions'. The scope ('shared, revisioned', 'Problems and Solutions') plus the companion hint 'fetch the likely ones' cleanly distinguishes it from the fetch/get_changes siblings.

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?

Gives explicit triggers ('error looks infrastructural, protocol- or tooling-related', 'before broad web research', 'retries/idempotency/versions/environments matter') AND explicit exclusions ('trivial syntax errors, simple edits, project-specific business decisions, obviously unique local issues'), plus a prescribed query escalation order. Nothing about when-to-use or when-not-to-use is left to inference.

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?

Annotations already disclose readOnlyHint=true, destructiveHint=false, and openWorldHint=false. The description adds meaningful behavioral details beyond that: it explicitly notes the unauthenticated case ('returns authenticated:false when no valid credential is configured') and lists the specific identity and scope information returned. This adds value, though it does not cover auth requirements or rate limits.

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, front-loaded with purpose and then usage. The first sentence is dense with an enumeration of return fields, which is informative but slightly heavy; overall it avoids filler and every sentence earns its place, though it could be tightened.

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 (no parameters, read-only, output schema exists), the description is complete: it states the purpose, when to call it, the safety profile, and the unauthenticated edge case. An agent has everything needed to select and 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?

The tool takes zero parameters, so there is no parameter semantics to explain. Per the scoring rule, a baseline of 4 is appropriate when there are no parameters; the description does not need to compensate for any schema gaps.

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 ('Show') and resource ('authenticated contributor identity'), and enumerates the exact information returned (operator boundary, scopes, credential expiry, write_epoch, publish/review behavior). However, it does not explicitly differentiate itself from the sibling tools (fetch, get_changes, search), so it falls short of the top score.

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?

Provides clear usage context: 'Call once before the first contribution in a session.' This tells the agent when to use it, but it offers no exclusions (when not to use) or alternative tools, which would be needed for a 5.

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