Confluence

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds valuable context: the default free model, the need for BYO API key for Anthropic (with payment implications), and the return structure. No contradictions with annotations.

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 three sentences, each adding distinct value: purpose, configuration, and use cases. No fluff; front-loaded with the core action. Perfectly concise.

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?

Despite having no output schema, the description explicitly lists the return structure (per-model {score, confidence, signals, raw_response} + combined view). With 4 parameters fully described in both schema and description, the description covers all necessary context for an agent to use the tool effectively.

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 input schema has 100% description coverage, but the description goes further by explaining the default model selection, the role of _apiKey, and providing example values for 'entity'. This adds meaning beyond the schema definitions.

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's purpose: probing LLMs for brand visibility and scoring it. It uses specific verbs ('Probe', 'score') and identifies the resource (LLMs, business/brand/product/topic). This distinguishes it from siblings like 'ask_pipeworx' (focused on a specific business) and 'scan_competitor_ai_presence' (likely a broader scan).

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 explains when to use the tool (AI-marketing audits, pre-launch checks, competitive monitoring) and how to configure it (default model, optional API key for Anthropic). It does not explicitly state when not to use it or compare against siblings, but the context is clear enough for an agent to decide.

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 for behavioral traits. It discloses that Pipeworx chooses the tool and fills arguments, indicating some delegation of decision-making, but does not detail data sources, privacy implications, or limits on question complexity.

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?

Description is three sentences plus examples. Front-loaded with core function, no redundancy, examples are concrete. Every sentence adds value.

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 a single required parameter, no output schema, and no annotations, the description is adequate. It explains the abstraction and gives examples, but could be more complete about what kinds of questions are out of scope or what happens if the best source fails.

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 baseline is 3. The description adds value by explaining that the parameter is a natural language question, with examples showing typical usage. This goes beyond the schema's 'Your question or request in natural language' by illustrating scope.

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's purpose: answering questions in plain English by selecting the best data source. It distinguishes itself from other tools by acting as an abstraction layer over schemas and tool browsing.

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 includes examples of appropriate questions, but does not specify when not to use this tool or mention alternatives. Given the sibling tools are mostly confluence and memory operations, the tool seems broadly applicable, but no explicit usage boundaries are provided.

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 declare readOnlyHint and openWorldHint. Description adds resolution logic (slug/URL/question), classification, fan-out to packs, and output format. Slightly lacks detail on what 'market-vs-model' comparison looks like.

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?

Single paragraph, front-loaded with purpose, no filler. Every sentence adds information.

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?

No output schema, but description explains return of evidence packet and model comparison. Fans out to packs and classification are covered. Could specify exact structure of evidence packet, but sufficient for an AI agent.

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 covers both parameters with descriptions. Description expands on market input types and explains depth options ('quick' vs 'thorough'). Adds value beyond schema, though depth defaults could be stated.

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 researches Polymarket bets by pulling Pipeworx data, resolving markets, classifying bets, and returning evidence. It distinguishes itself from sibling tools like compare_entities and validate_claim.

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?

Explicitly lists use cases: 'should I bet on X?', 'what does the data say?', 'is there edge?'. Also frames it as the core demo product, implying advantage over manual pack discovery.

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 discloses output format (paired data + URIs) and data sources (SEC EDGAR, FDA), but lacks details on error handling, pagination, or what happens if entities are not found. It implies read-only but does not guarantee it.

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 concise (4 sentences), front-loaded with purpose, and efficiently conveys all key information without 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?

For a tool with 2 parameters and no output schema, the description adequately explains the two type-specific outputs and the output format (paired data + URIs). It could elaborate on the structure of 'paired data' but is otherwise complete 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 coverage is 100% with descriptions for both parameters. The description adds value by explaining the specific data fields returned for each type and the expected format for values (tickers/CIKs, drug names), going 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?

The description clearly states the tool compares 2-5 entities side by side, specifies two entity types with distinct data returned, and notes it replaces multiple sequential calls. This distinguishes it from sibling tools (Confluence, memory, etc.).

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 provides clear context for when to use the tool (side-by-side comparison, efficiency gain) but does not explicitly state when not to use it or recommend alternatives. However, its uniqueness among siblings makes guidance adequate.

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 are empty, so the description carries the full burden. It states the return values (ID, title, URL) but does not disclose side effects (e.g., notifications, permissions required) or behavioral traits like synchronous creation. No contradictions.

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: first states purpose, second states output. It is concise and front-loaded, but could be slightly more structured by including parameter hints. No wasted words.

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 moderate complexity (5 parameters, 3 required, no output schema), the description is adequate but not complete. It lacks details on return format beyond ID/title/URL, and does not mention error conditions or rate limits. The schema covers parameter descriptions, so the description is not insufficient.

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 baseline is 3. The description adds no extra meaning beyond the schema; it does not explain the 'body' format (storage format) or optional parameters like 'status' default. The return description hints at the output but not parameter details.

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 uses a specific verb ('Create') and resource ('Confluence page'), and states what the tool returns ('created page ID, title, and URL'). It clearly distinguishes from sibling tools like 'confluence_get_page' (retrieval) and 'confluence_search' (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 does not provide explicit guidance on when to use this tool versus alternatives. It does not mention prerequisites (e.g., space must exist) or when not to use it. However, the context of creating a page is clear, and sibling names imply distinct purposes.

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 provided, so description must cover behavior. It states it returns specific fields and mentions body_format parameter, but does not disclose potential errors, rate limits, or authentication needs. Acceptable for a simple read operation.

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 concise sentences, no fluff. First sentence states purpose, second lists key return fields. Perfectly 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?

Given low complexity (2 params, no output schema, no nested objects), the description is complete enough. It explains what the tool returns and mentions the optional body_format parameter. A brief note on possible error conditions would push to 5.

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. The description adds no extra meaning beyond the schema; it only repeats that body_format controls return format. 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?

Clearly states the verb 'get' and resource 'single Confluence page by ID', and lists the exact returned fields (title, body content, status, version, space info). Distinct from siblings like confluence_create_page and confluence_list_pages.

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?

Implied usage: use when you need details of a specific page by ID. No explicit guidance on when not to use or alternatives (e.g., for listing pages use confluence_list_pages, for search use confluence_search).

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. It mentions return fields (ID, title, status, version) which is helpful, but does not disclose pagination behavior, sorting details beyond what's in schema, or whether the tool is read-only. The description adds moderate value but lacks deeper behavioral context.

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 a single, concise sentence that states the core purpose and return fields. It is front-loaded and efficient, with no superfluous text. Could be slightly improved by front-loading the return fields more explicitly, but overall well-structured.

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 has 3 parameters with full schema coverage and no output schema, the description provides the basic purpose but lacks completeness. It doesn't mention pagination behavior, error conditions, or permission requirements. It is minimally adequate but leaves gaps for an AI agent.

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 all parameters (space_id, sort, limit). The description does not add new semantics beyond the schema. Baseline score of 3 is appropriate as the description does not compensate beyond what's already in 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?

The description clearly states the verb 'list' and resource 'pages in a Confluence space', and lists the return fields (page ID, title, status, version). It distinguishes from sibling tools like 'confluence_get_page' (single page) and 'confluence_search' (search across content) but does not explicitly differentiate from 'confluence_list_spaces' (lists spaces, not pages).

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 use when you need to list pages in a specific space (via space_id). It does not provide explicit when-to-use vs alternatives, such as when to use 'confluence_search' instead for query-based retrieval. No exclusions or prerequisites are mentioned.

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 are empty, so the description carries the burden. It states that it returns ID, key, name, type, and status, but does not disclose pagination behavior (only limit param), rate limits, or whether it returns all spaces or just accessible ones. It is minimally transparent.

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 the action and output, no redundant words. Every sentence adds value.

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 it is a simple list tool with no output schema, the description covers the purpose and return fields adequately. However, it lacks details on edge cases (e.g., empty result, error handling) and does not mention if type filtering is required or optional.

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 baseline is 3. The description does not add meaning beyond the schema; it repeats the return fields but does not elaborate on parameter usage or behavior beyond what schema provides.

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 action (list), resource (Confluence spaces), and what is returned (space ID, key, name, type, and status). It is specific and distinguishes from siblings like 'confluence_list_pages' which lists pages, not spaces.

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 basic usage but does not provide guidance on when to use this tool vs alternatives. For example, it doesn't mention that for more advanced search or filtering, one might use 'confluence_search' instead.

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 are absent, so the description bears the burden of behavioral disclosure. It correctly indicates the tool returns matching pages with specific fields, which is adequate. However, it does not disclose potential side effects (likely none), rate limits, or authentication needs beyond what is implicit in the tool's name.

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 no wasted words. The first sentence states the action and language, the second specifies the output structure. All information is essential and 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?

Given no output schema, the description covers return fields adequately. The tool has low complexity (2 parameters, both described in schema). The description is complete enough for an agent to use the tool correctly, though it could mention default limit or pagination behavior.

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 baseline is 3. The description adds value by explaining the return fields (ID, title, space, excerpt), which are not in the input schema. However, it does not elaborate on the CQL syntax beyond the schema's 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 specifies a clear verb ('Search') and resource ('Confluence content'), and mentions the specific query language (CQL) and return fields (ID, title, space, excerpt). It distinguishes itself from sibling tools like confluence_get_page (single page) and confluence_list_pages (list without search) by emphasizing search via CQL.

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 this tool is for searching Confluence using CQL, but does not explicitly state when to use it versus alternatives like confluence_list_pages (for browsing without CQL) or confluence_get_page (for a known page). No exclusion criteria or prerequisites are provided.

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 clearly states the tool returns the most relevant tools with names and descriptions, and that it searches by describing what you need. While it doesn't mention performance or side effects, it is transparent about the core behavior. A small deduction for not explaining what happens if the query is vague or no matches.

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 valuable: first states the action, second describes the return, third gives usage guidance. No wasted words. Well structured and 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?

Given that the tool is simple (2 parameters, no nested objects, no output schema), the description is largely complete. It covers purpose, input format, and usage context. However, it does not mention the output format or that it may return empty results, which would be helpful for an AI agent. Slight deduction for missing that detail.

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 description adds value beyond schema: it explains that the query should be a natural language description and gives examples. The limit parameter is also described in schema, but the description reinforces its purpose. Could be improved by noting that limit defaults to 20.

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?

Clearly states it searches the Pipeworx tool catalog using natural language and returns relevant tools with names and descriptions. Differentiates from siblings as a discovery/search tool, distinct from query tools like ask_pipeworx and knowledge tools like recall.

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?

Explicitly instructs to call this first when there are 500+ tools and need to find the right ones. Provides strong usage context, though does not mention when not to use it or alternatives. However, the directive is clear and actionable.

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 bears full responsibility for behavioral disclosure. It lists the data sources returned (SEC filings, XBRL, patents, news, LEI) and notes the output format (pipeworx:// citation URIs). It does not mention performance guarantees, rate limits, or potential side effects, but it does imply the call may be slow by referencing bundling. It could be more explicit about expected response time or size.

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 concise—three sentences that front-load the main purpose and list key features. No redundant words; every sentence adds value. It is well-structured and easy to scan.

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 complexity (aggregating multiple data sources) and the absence of an output schema, the description provides a good overview of what is returned (data types and citation URIs). However, it does not specify the structure of the response (e.g., whether it is a single object with multiple fields or a list of results). This would help an agent parse the output. Still, it is largely complete for making an informed invocation decision.

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 input schema has 100% description coverage, so the baseline is 3. The description adds context by explaining the tool's behavior with the parameters (e.g., fetching multiple data sources), but it largely restates the schema (type only company, value ticker/CIK, names not supported). It does not provide additional meaning beyond what's in the schema fields.

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 returns a full profile of an entity across multiple data sources, listing specific data types (SEC filings, revenue, patents, news, LEI). It distinguishes itself from sibling tools like resolve_entity and compare_entities by explaining it replaces many sequential calls, and explicitly directs agents to usa_recipient_profile for federal contracts. The verb 'get profile' is specific and resource-oriented.

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 provides explicit when-to-use context: for full company profiles by ticker or CIK. It also advises when not to use it—for federal contracts, use usa_recipient_profile directly because bundling is too slow. Additionally, it instructs to use resolve_entity if only a name is available, offering a clear alternative.

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 must disclose behavioral traits. It states deletion but does not mention if it is irreversible, whether confirmation is needed, or what happens if the key does not exist. Lacks details on safety or side effects.

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?

Single sentence, zero waste, front-loaded with verb and object. Every word serves a purpose.

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?

Tool is simple (one required param, no output schema), but the description omits behavioral details (e.g., idempotency, error handling) that would be useful for a deletion tool. Sibling tools for memory (recall, remember) lack contrast.

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% (the schema fully describes the 'key' parameter). The description does not add new meaning beyond the schema, so 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 clearly states the action ('Delete'), resource ('stored memory'), and scope ('by key'). It distinguishes from siblings like 'recall' and 'remember' by specifying deletion rather than retrieval or storage.

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 on when to use this tool versus alternatives. It does not mention prerequisites (e.g., memory must exist), nor does it contrast with other memory operations like 'recall' or 'remember'.

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?

Describes the actual steps: fetches page, extracts title/description/key links, emits markdown format. This adds detail beyond the annotations (readOnlyHint, idempotentHint) by explaining the processing pipeline. No contradiction with annotations.

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: first sentence defines core action and audience, second details steps and use cases. No filler, 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?

Covers purpose, behavior, output format, and multiple use cases. For a simple read-only tool with annotations providing safety guarantees, only minor gaps remain (e.g., error handling for invalid URLs).

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 descriptions already cover both parameters well (100% coverage). The main description reinforces the url's purpose and the max_links default/range, but does not add new parameter-specific information beyond what the schema provides.

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?

Clearly states the verb 'generate', the resource 'llms.txt file', and the target audience 'AI crawlers (ChatGPT, Claude, Perplexity)'. Distinct from all sibling tools which perform different operations like AI visibility checking, Confluence management, or betting research.

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?

Explicitly lists three use cases: indexing client sites, drafting personal projects, and auditing competitors. Does not mention when not to use or alternatives, but the sibling tools are sufficiently different that no exclusion is 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 provided, so description bears full burden. Discloses rate limit (5 messages per identifier per day) and that it is free. Also advises on content format. Adequate for a simple feedback 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?

Four sentences with no wasted words. Front-loaded with primary purpose. Efficient and clear.

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?

Covers purpose, usage guidelines, rate limiting, and content restrictions. No output schema needed. Sufficient for an agent to correctly select and invoke 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?

Schema coverage is 100% with descriptions. Description adds guidance on how to fill the message parameter (describe tool usage, avoid verbatim prompts), which adds value beyond 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?

Clearly states action 'Send feedback' and resource 'Pipeworx team'. Lists specific use cases: bug reports, feature requests, missing data, praise. Distinct from sibling tools which are for querying or creating content.

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?

Explicitly says 'Use for...' and gives a negative instruction to not include end-user's prompt verbatim. Also mentions rate limiting. Does not explicitly compare to alternatives but is self-contained.

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 provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds value by disclosing caching behavior (5min-1h), data source (CF analytics-engine), and privacy (no PII), which go beyond the annotations.

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 three sentences, front-loaded with the main output, and every sentence adds substantive value without redundancy. It is efficiently structured for quick parsing.

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 parameter, no output schema) and rich annotations, the description fully covers what the tool does, its inputs, behavior, and use cases. No additional information is needed.

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 single parameter 'window' has 100% schema coverage with an enum. The description adds meaning by explaining the semantics of shorter vs. longer windows, which enhances the agent's understanding 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?

The description clearly states the tool returns top tools, packs, and call volume over a window. It uses specific verbs ('returns') and resources ('what other AI agents are calling'), and distinguishes itself from siblings by focusing on aggregated usage trends rather than individual queries.

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 lists three use cases (discovering hot data sources, confirming canonical choice, seeing alignment), providing context on when to use. However, it does not mention when not to use or suggest alternatives, though siblings like 'discover_tools' exist.

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 indicate readOnlyHint, openWorldHint, destructiveHint. The description adds significant behavioral context: it walks child markets, extracts dates/thresholds, sorts them, and reports violations. There is no contradiction with annotations.

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 a single, well-structured paragraph with no filler. It front-loads the purpose, explains the logic, and describes the output. Every sentence contributes to understanding.

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 complexity and lack of output schema, the description is remarkably complete. It explains the arbitrage concept, input format, internal processing, and output structure (list of {market_a, market_b, gap_pp, suggested_trade}). No additional information is needed.

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?

Only one parameter 'event' with 100% schema description coverage. The description adds meaning by explaining expected input (slug or URL) with an example, and how the parameter is used within the tool's logic.

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's purpose: finding arbitrage opportunities by checking monotonicity violations. It specifies the action ('find'), the resource ('arbitrage opportunities within a Polymarket event'), and the mechanism. This effectively distinguishes it from siblings like polymarket_edges.

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 explains when to use the tool (for events with multiple ordered markets) and the underlying principle (monotonicity), providing clear context. It does not explicitly state when not to use it or list alternatives, but the explanation is sufficient for appropriate invocation.

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?

Beyond annotations (readOnlyHint, openWorldHint), the description details the step-by-step process: scanning top markets, grouping by asset, fetching price history once, computing model probability, ranking by edge, and returning top N with direction. It also notes model source (FRED, coinpaprika) and that V1 is crypto-focused, giving full behavioral 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 well-structured with a clear first sentence defining purpose, followed by technical details, output description, and use-case framing. It is slightly longer than necessary but every sentence contributes value, and it avoids repetition with annotations/schema.

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?

Despite lacking an output schema, the description fully describes the return value (top N ranked by edge with suggested trade direction) and explains the computation sufficiently. Given the tool's complexity (external data, model, ranking), the description leaves no critical gaps for an agent to decide 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?

Schema coverage is 100% with adequate descriptions for all three parameters. The description adds minimal new meaning beyond restating defaults and constraints already in the schema, so 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 precisely states the tool scans high-volume Polymarket markets and returns those with greatest disagreement between Pipeworx model and market price. It specifies the scope (crypto-price bets, lognormal model from FRED + coinpaprika) and differentiates from siblings like 'polymarket_arbitrage' by focusing on edge magnitude and trade direction.

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 targets the 'what should I bet on today' use case, telling agents to discover opportunities without manual paging. While it doesn't list alternative tools or when-not-to-use, the context is clear enough to guide selection among siblings.

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 read-only and idempotent. Description adds details on modes, return format (leg-by-leg prices in 0-1, spread in percentage points), and mapping logic. No contradictions.

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?

Concise single paragraph with clear front-loading of purpose, two modes, and return values. Every sentence adds necessary information; no wasted words.

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?

Despite no output schema, the description fully explains return format and covers all behavioral aspects (modes, parameter interactions). Sufficient for an agent to invoke 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 coverage is 100% with good descriptions. The description adds value beyond schema by explaining the two mode concept and how explicit parameters override topic mapping, enriching usage understanding.

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 computes cross-venue spread between Kalshi and Polymarket, with specific verb+resource and two modes. It distinguishes from siblings like polymarket_arbitrage by focusing on spread calculation and explicit mapping.

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 context on when to use (arbitrage opportunity due to pricing gaps) and explains two modes. Lacks explicit when-not-to-use or comparison with sibling tools, but the context is clear enough for an agent to decide.

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?

The description indicates this is a retrieval operation (read-only), which is consistent with the absence of destructive annotations. It adds context about cross-session persistence ('saved earlier in the session or in previous sessions'). No contradictions with annotations (none provided).

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 the core functionality. No redundant information. Every word adds value.

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 simple tool (1 optional parameter, no output schema), the description is complete. It explains both invocation modes (with/without key) and the cross-session persistence. No additional details are needed for effective use.

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% with a single parameter 'key' well-described in the schema. The description adds value by explaining the behavior when key is omitted (list all memories), which is not in the schema. This clarifies the optional nature beyond the required array.

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 retrieves a memory by key or lists all memories when key is omitted. It specifies the resource ('memory') and the action ('retrieve' or 'list'). This distinguishes it from siblings like 'remember' (store) and 'forget' (delete).

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 explains when to use the tool: 'to retrieve context you saved earlier'. It implies that omitting key lists all memories, but does not explicitly state when not to use it or compare to alternatives. However, given sibling names, the context is clear enough.

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 provided, the description fully covers behavioral details: it fans out to multiple sources in parallel, accepts specific date formats, and returns structured output with URIs. It does not mention idempotency or limitations, but the information is adequate for an agent to understand side effects.

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 concise at about 5 sentences, front-loads the purpose, and efficiently covers all key aspects without redundancy. Every sentence contributes meaning.

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 sufficiently covers the tool's functionality given its complexity and lack of output schema. It explains the parallel fan-out, input formats, and return structure. Minor gaps like error handling or explicit permission requirements are absent but not critical for typical use.

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%, but the description adds value by explaining accepted formats (ISO dates and relative strings) and providing typical usage hints (e.g., '30d' or '1m' for monitoring). This goes beyond the schema's short descriptions.

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's purpose: retrieving recent changes for an entity since a given time. It specifies the resource (entity) and action (what's new), and distinguishes from siblings like entity_profile by targeting change-monitoring workflows. Use cases are explicitly mentioned.

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 provides clear usage scenarios ('brief me on what happened with X' or change-monitoring workflows). However, it does not explicitly state when not to use this tool or mention alternatives among siblings, though the purpose is sufficiently distinct.

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?

Since no annotations are provided, the description carries the full burden. It discloses that the tool stores data in session memory, notes persistence differences for authenticated vs. anonymous users, and implies the data can be retrieved later. No behavioral contradictions are present.

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 three sentences long, front-loaded with the core action, and every sentence adds value: what it does, when to use it, and persistence behavior. No wasted words.

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 that there is no output schema and no nested objects, the description adequately covers what the tool does and its persistence behavior. However, it does not specify whether the tool overwrites existing keys or returns any confirmation, which would be helpful for completeness.

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 input schema covers 100% of parameters with descriptions. The description adds context by explaining the purpose of key-value pairs (e.g., subject_property, target_ticker) and the nature of the value (any text). This goes beyond the schema's parameter descriptions.

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 verb 'store' and resource 'key-value pair in session memory'. It explicitly mentions the use case: saving intermediate findings, user preferences, or context across tool calls, which distinguishes it from siblings like 'forget' and 'recall'.

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 explains when to use this tool (to save intermediate findings, preferences, context) and provides persistence context (authenticated users get persistent memory; anonymous sessions last 24 hours). However, it does not explicitly say when not to use it or mention alternatives among siblings.

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 present, so the description carries full burden. It discloses return fields (ticker, CIK, name, URIs) and version constraints (v1, company only). However, it omits details on authentication, error handling, or idempotency, which are important for a tool with no annotations.

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 about 50 words, front-loads the main purpose, and includes necessary details like version and return values. It is efficient without being wasteful.

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 (2 params, no output schema, no annotations), the description provides enough context to understand usage and return format. It mentions the benefit (replaces 2-3 calls). Minor gaps exist around edge cases like multiple matches.

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 input schema has 100% coverage with descriptions for both parameters. The description adds value beyond schema by providing examples (AAPL, CIK, Apple) and clarifying version support for 'type'. This aids agent understanding.

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 resolves an entity to canonical IDs across Pipeworx data sources, providing a specific verb and resource. It includes an example for company type. However, it does not differentiate from sibling tools like ask_pipeworx, but the context shows no direct 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?

The description implies when to use (single call replacing multiple lookups) but does not explicitly state when not to use or provide alternatives. The sibling list includes ask_pipeworx, which could be a general query tool, but no guidance is given.

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 indicate readOnly, openWorld, idempotent, non-destructive. Description adds that it probes each entity with ai_visibility_check and returns a ranked list with scores, confidence, and signal density. No contradictions.

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?

Concise 4-sentence description, front-loaded with main purpose. Every sentence provides essential information: what it does, how it works, use case, and output. No wasted words.

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?

No output schema, but description specifies output: 'ranked list with score, confidence, signal density per entity'. Covers all key aspects: usage, parameters, behavior, and output. Complete for a tool with 4 parameters and moderate complexity.

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?

All 4 parameters have schema descriptions (100% coverage). The description adds context: entities are compared, first is subject, models require optional API key, context disambiguates. Adds value beyond 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?

The description clearly states the verb 'Compare' and the resource 'AI visibility across multiple entities side-by-side', distinguishing it from the sibling 'ai_visibility_check' which checks a single entity. It specifies the comparison and ranking output.

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 explicit use case: 'competitive AI-marketing audits' with an example question. Implicitly differentiates from ai_visibility_check by focusing on multiple entities, but does not explicitly state when not to use or list alternatives.

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 explains the tool uses SEC EDGAR+XBRL, returns a verdict with citation and delta, and implies a read-only operation. It doesn't explicitly confirm non-destructiveness, but the context suggests no side effects.

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 concise (four sentences) with no redundant information. It front-loads the main purpose and includes a note on efficiency, making every sentence earn 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?

Given the simple input schema and no output schema, the description adequately explains the return values (verdict, value, citation, delta) and the source domain. It lacks details on error handling or edge cases, but is sufficient for selection and 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?

Schema coverage is 100% with a description for 'claim'. The tool description adds value by giving examples and specifying the kind of claims accepted (company-financial), which goes beyond the schema's generic description.

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 it fact-checks natural-language claims against authoritative sources, specifies the domain (company-financial claims via SEC EDGAR+XBRL), and lists the output (verdict, value, citation, delta). It distinguishes itself from sibling tools like 'ask_pipeworx' and 'compare_entities' by focusing on claim validation.

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 mentions it replaces 4-6 sequential agent calls, implying the context where it's beneficial. However, it does not explicitly state when not to use it or provide alternatives, though the domain restriction is implicit.

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