Govcon Intel

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds details about default model, optional Anthropic probing with BYO key, and return structure with per-model and combined views, providing significant behavioral context beyond 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 coherent paragraph of about 110 words, structured with core action first, then model details, return format, and use cases. No superfluous sentences.

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 explains return values (per-model and combined). It covers default vs. optional models, parameter roles, and use cases, making it complete for the tool's 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?

Schema coverage is 100% with descriptions for each parameter. The description enriches understanding by providing examples for entity, supported models, and the role of _apiKey and context, adding value 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 it probes LLMs for knowledge about an entity and scores visibility per model. It distinguishes from sibling tools which focus on domains like PipeWorx or GovCon, making the purpose unique and well-defined.

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 notes use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. It explains the default model and requirement for API key for Anthropic, but does not provide explicit when-to-use vs. alternatives or exclusions.

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, openWorldHint, idempotentHint, and destructiveHint=false, establishing safety and idempotency. The description adds valuable behavioral context: it routes queries to 3,745 tools, fills arguments, and returns structured answers with stable citation URIs. 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 paragraph that efficiently front-loads the most critical guidance (prefer over web search), then lists supported domains and examples. Every sentence adds necessary detail without fluff. 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 tool's complexity (3,745 tools across 884 sources) and the absence of an output schema, the description provides sufficient context: it explains what the tool does, when to use it, what kinds of questions are suitable, and what the output looks like (structured answer with pipeworx:// URIs). No gaps for an agent to misuse it.

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 all parameters being aliases for 'question', and the schema provides clear descriptions. The description enhances this by explaining the natural language usage and providing examples. It adds value beyond the schema but the schema already does a good job.

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 strong verb ('ask'), specifies the resource ('Pipeworx'), and clearly contrasts with web search. It lists numerous example domains (SEC filings, FDA, FRED, etc.) and gives concrete query examples, making the purpose unmistakable and differentiating it from siblings like 'deep_research' or '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 states 'PREFER OVER WEB SEARCH' and provides detailed when-to-use guidance covering many question formats ('what is', 'look up', 'find', etc.). Also gives multiple real-world examples, ensuring an agent can decide appropriately between this and alternative 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?

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint. Description adds behavioral context: costs one extra LLM call, returns explicit refusal reasons, extracts answer only from tool result, and lists possible refusal reasons.

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 5 sentences, front-loaded with purpose and behavior. Every sentence adds value, but slightly longer than minimal. Clear structure with use cases and trade-off.

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, return format, error modes, and trade-off with sibling. No output schema, but description adequately describes return structure. For a complex tool with 6 parameters, this is comprehensive.

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. Description notes question parameter accepts natural language and aliases, but this is already in the schema's description of the question property. No additional meaning 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 specifies 'hallucination-resistant answer mode' for high-stakes reads. Distinguishes from sibling ask_pipeworx by explaining the extra LLM call and refusal mechanism.

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 states when to use: for quoted/cited/acted-on answers where facts must not be invented. Also advises preferring ask_pipeworx for casual lookups, providing clear when-not guidance.

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 extensively discloses behavioral traits beyond annotations (readOnlyHint, idempotentHint, etc.), including resolver contract, parent event extraction, safety mechanisms (low-confidence short-circuit, closed markets), resolution-rule risk, and news fallback behavior. 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 long but well-structured with clear sections (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES, etc.). It is front-loaded with the core purpose and usage. Some redundancy in examples could be trimmed, but overall it balances thoroughness with organization.

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 documents return shapes (result.market, result.analysis, result.evidence, resolver contract, parent event, news fields). It covers edge cases (low confidence, closed markets, wide spreads, cancellation rules) and provides sufficient context for an agent to use the tool 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 description adds significant meaning beyond the input schema: explains how 'market' accepts slug, URL, or question text; details 'depth' options (quick vs thorough); and clarifies 'include_raw' behavior and recommended usage. Schema coverage is 100%, but description enriches each parameter with examples and context.

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 a Polymarket bet by pulling Pipeworx data in one call. It specifies the verb 'research', the resource 'Polymarket bet', and the method 'pull Pipeworx data'. It distinguishes itself from siblings like 'ask_pipeworx' and 'polymarket_edges' by focusing on a single bet research workflow.

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 states when to use: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' Also provides detailed fan-out examples and context for when the tool is appropriate, making usage conditions very clear.

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 goes beyond the readOnlyHint, openWorldHint, idempotentHint, and destructiveHint annotations by detailing the specific data retrieved (e.g., financial metrics from SEC EDGAR/XBRL, adverse-event counts from FAERS), handling of fiscal years, sorting by primary metric, and inclusion of citation URIs, providing comprehensive 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 efficiently structured, starting with concrete example phrases, then stateing the core action, and detailing behavior for each entity type. Every sentence adds value without redundancy, achieving high information density in a compact form.

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 (two entity types with different data sources) and no output schema, the description fully explains the return format (paired data with citation URIs) and sorting, and notes it replaces multiple sequential lookups, leaving no gaps for the 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?

The description adds significant meaning beyond the input schema: it specifies the formats for 'values' (tickers/CIKs for company, drug names) and explains what data each 'type' returns, enriching the schema's minimal descriptions of 'Entity type' and 'array of strings'.

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 clear example phrases like 'Compare X and Y' and explicitly states the tool performs side-by-side comparisons of 2-5 companies or drugs in a single parallel call, distinguishing it from sibling tools like entity_profile for single entity lookups.

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 says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' providing clear guidance on when to use this tool versus alternatives, and implies that for single entity lookups one should use entity_profile.

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, openWorldHint, idempotentHint, and destructiveHint=false. The description adds substantial context: it returns structured findings with verbatim evidence, confidence, source, and gaps for unanswered facets, never invents information, requires a Pipeworx account, and notes expected duration (15-60s) and depth limitations (thorough requires paid plan). 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?

Description is informative and front-loaded with the core purpose. While it is relatively long, every sentence adds value. Minor improvements could be made for brevity, but structure is clear and effective.

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 (multi-source parallel research), the description covers behavior, prerequisites (Pipeworx account), limitations (paid plan for thorough), timing (15-60s), and output structure (findings packet). No output schema, but description suffices for agent understanding.

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 basic parameter descriptions. The description enriches the meaning by specifying that 'depth' corresponds to number of facets (quick=3, standard=5, thorough=8) and that 'thorough' requires a paid plan. This goes beyond schema information.

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 explicitly states the tool's core function: 'Grounded multi-source research in ONE call.' It explains the process of decomposition, parallel routing, and extraction of grounded answers. It also distinguishes itself from siblings like 'ask_pipeworx' for single lookups.

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 guidance on when to use: for broad or multi-part questions such as 'compare X and Y' or 'research regulatory + financial + market picture.' Also explicitly states alternatives: 'use ask_pipeworx for single lookups.'

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, idempotentHint, and destructiveHint=false. The description adds concrete behavioral details: returns top-N tools with full schemas and curated examples, results are ready to call directly. 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?

Two sentences, well-structured with front-loaded purpose. The list of domains is somewhat lengthy but necessary for context. Could be slightly more concise, but overall efficient.

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 the return value: names, descriptions, full input schemas with curated examples, ready to call. It addresses all likely agent needs for this discovery 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%, so baseline is 3. The description adds meaningful context: explains that query takes natural language, gives usage examples, and clarifies aliases (though aliases are also in schema). This extra guidance justifies a 4.

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 finds tools by describing data or task, listing many domains. It distinctly positions itself as a discovery tool, separate from sibling tools like deep_research or entity_profile which are specific tasks.

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 'Call this FIRST when you have many tools available and want to see the option set' and 'Use when you need to browse, search, look up, or discover what tools exist for: ...' – provides clear when-to-use guidance and suggests it as an initial step.

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 indicate read-only, open-world, and idempotent behavior. The description adds specific behavioral details: it fans out across multiple sources in one parallel call, returns a structured list of fields (cik, company_name, filings, fundamentals, patents, news, LEI), and warns that patent retrieval may soft-fail. 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 longer but efficiently structured. It front-loads example queries, then describes the fan-out behavior and return fields. While some details (like the patent API sunset) could be a separate line, the information is relevant and well-organized. It earns its length by being packed with useful context.

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 clearly enumerates the return fields (cik, filings, fundamentals, patents, news, LEI) and their sources. It covers edge cases (patent API failure, name not supported) and provides enough detail for an agent to decide whether to call this tool. Minor gap: no mention of pagination or max size for filings, but 'up to 5' is specified.

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, but the description adds practical context: the 'value' parameter accepts tickers or zero-padded CIKs (not names), and the 'type' parameter is currently limited to 'company'. This adds meaning beyond the schema's basic descriptions, though the schema already describes both parameters well.

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 concrete examples like 'Tell me about X' and 'company profile for Microsoft' to clearly define the tool's purpose: generating a comprehensive cross-source profile of a US public company. It explicitly states it returns data from SEC EDGAR, XBRL, USPTO, news, and GLEIF, distinguishing it from sibling tools like 'compare_entities' or 'deep_research' which serve different purposes.

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 guidance on when to use: 'ALWAYS PREFER over chaining single-pack lookups' when the user asks for a holistic view. It also specifies when not to use: if only a name is available, use 'resolve_entity' first. It further notes the patent API sunset as a potential limitation, helping the agent set expectations.

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 indicate destructiveHint=true and idempotentHint=true. Description confirms 'Delete' and adds context about clearing sensitive data, but does not add major new behavioral information beyond 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, front-loaded purpose, no wasted words. Every sentence 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 single-parameter destructive tool with no output schema, the description fully covers purpose, usage, and behavioral context. No gaps.

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 'key' parameter described as 'Memory key to delete'. The tool description does not add additional parameter meaning beyond the schema, so baseline of 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 clearly states the tool deletes a memory by key, using a specific verb and resource. It distinguishes from siblings 'remember' and 'recall' by being the deletion counterpart.

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 contexts for use: stale context, task completion, or clearing sensitive data. Also advises pairing with remember and recall. Lacks explicit when-not-to-use guidance, but is sufficient.

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 indicate readOnlyHint, idempotentHint, and non-destructive behavior. The description adds behavioral details: fetches the page, extracts title/description/key links, and outputs markdown. This provides useful insight into the tool's operation beyond the annotations, though it omits potential error conditions 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?

The description is extremely concise: two sentences that front-load the core purpose and quickly add implementation details and use cases. Every sentence is meaningful with no redundancy or filler.

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 parameters, no output schema), the description adequately covers its functionality and output format. It mentions the output is a single text blob in llms.txt markdown format, which is sufficient. Missing details about error handling or edge cases, but overall contextually complete for a straightforward 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?

Both parameters (url and max_links) are fully described in the input schema (100% coverage). The description does not add new semantic details beyond the schema, so it meets the baseline but does not surpass it.

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: generating a llms.txt file for a URL to aid AI crawlers. It specifies the action (generate), resource (llms.txt), and target audience (ChatGPT, Claude, Perplexity). However, it does not explicitly differentiate from sibling tools like ai_visibility_check or scan_competitor_ai_presence, which have overlapping themes.

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 lists three use cases (client's site, own project, competitor audit) which provide context for when to use the tool. However, it does not mention when not to use it or suggest alternative tools among the siblings. The guidance is present but not comprehensive.

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 and destructiveHint=false, so safety profile is clear. The description adds that the tool 'returns spending trends, recent awards, SBIR stats, and top contractors by volume', providing useful behavioral context beyond 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 two sentences with no wasted words. The verb and resource are front-loaded, making it easy to scan. 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?

For a simple query tool with one parameter and an output schema, the description covers what the tool returns. It doesn't mention pagination or error handling, but these are not critical given the straightforward nature and presence of an output schema.

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 description does not need to explain the 'agency' parameter beyond what's in the schema. The description includes example values, which are also in the schema's examples array, adding marginal value. 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 verb 'Get' and specific resource 'contracting activity and market insights for a federal agency', and provides examples (Department of Defense, NASA). It distinguishes from siblings like govcon_contractor_profile which focuses on individual contractors.

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 usage for federal agency overviews but does not explicitly state when not to use or mention alternatives. Sibling tools like govcon_contractor_profile or govcon_opportunity_scan provide different foci, but the description offers no guidance on selecting between them.

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, openWorldHint, idempotentHint, and destructiveHint. The description adds specific outputs like 'certifications, past performance, award amounts, and contract count', which provides extra behavioral context beyond 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 extremely concise, using only two sentences. The first sentence states purpose and outputs, and the second states usage context. 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?

Given the presence of annotations and an output schema, the description adequately covers purpose and usage. It could mention the data source (SAM.gov), but overall it is complete enough for a tool with good annotations.

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 parameters have adequate descriptions. The description does not add additional parameter-level meaning beyond what the schema provides, 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 uses the specific verb 'Vet' and resource 'government contractor's registration, federal awards, and spending history', clearly differentiating from sibling tools like 'govcon_agency_landscape' and 'govcon_opportunity_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 explicitly states 'Use when assessing vendor credibility or experience', providing clear context. It does not explicitly mention when not to use or list alternatives, but the context is sufficient.

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?

Description adds significant behavioral context beyond annotations: it explains the API key requirement for open/set-aside opportunities, that without the key those legs return unavailable with a reason, and that SBIR solicitations and awards work without a key. This complements the readOnlyHint and idempotentHint 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?

Three sentences, each serving a distinct purpose: main action, return details, and API key requirement. Front-loaded with primary purpose; no redundant or unnecessary 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?

Given the complexity (3 parameters, output schema exists), the description covers key behaviors (key requirement, two data sources with different auth needs). It mentions return fields like deadlines and SBIR solicitations. Could benefit from mentioning pagination or result limits, but overall sufficient.

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 all three parameters. The description adds value by explaining the consequence of omitting _apiKey and the types of set-asides, but the schema already provides basic definitions. The additional context is helpful but not essential.

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?

Description clearly states it searches open government contracts and grants by keyword or agency, returning specific details like set-asides, deadlines, and SBIR solicitations. This distinguishes it from siblings like govcon_agency_landscape and govcon_contractor_profile, which focus on different aspects of government contracting.

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 implicitly indicates use for searching contracts/grants but lacks explicit guidance on when to use versus alternatives or when not to use. It does not contrast with sibling tools or provide exclusion criteria.

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 and destructiveHint; description adds return fields and default active-only behavior. No contradiction, but adds limited new behavioral info beyond strong 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 states purpose and output fields, second gives usage guidance. No wasted words, 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?

Complete for a simple list tool with one parameter and strong annotations. Covers purpose, output fields, usage context. No missing information.

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%; the parameter include_inactive is documented there. Description mentions it but adds no extra syntax or formatting details. Baseline score 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 'List the caller's active subscriptions' with specific verb and resource, clearly distinguishing from sibling tools like subscribe and unsubscribe.

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 context: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' Implicitly guides when not to use (instead of subscribe/unsubscribe), but lacks explicit when-not statement.

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?

Discloses behavioral traits beyond annotations: rate-limited to 5 per identifier per day, free, doesn't count against tool-call quota. 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?

Description is front-loaded with purpose and usage, but slightly verbose. Every sentence adds value, though could be tightened. Still 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?

Despite no output schema, the description explains expected outcomes (team reads digests daily, signal affects roadmap). Covers all necessary context for a feedback 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 has 100% coverage, but description adds meaning by explaining the enum values (bug/feature/data_gap/praise) and advising to describe issues in terms of tools/packs. The context object's purpose is also elaborated.

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: sending feedback (bugs, features, data gaps, praise) to the Pipeworx team. It distinguishes from sibling tools by its specific feedback focus and explicit use cases.

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 guidance on when to use each feedback type (bug, feature/data_gap, praise) and what not to do (avoid pasting end-user prompts). Also mentions rate limits and that it's free and quota-free.

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 indicate read-only and idempotent behavior. The description adds valuable context: data derivation from CF analytics-engine, no PII, and caching duration (5min-1h). 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 concise (4 sentences) with clear bullet points for use cases. Every sentence adds value with no 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?

Given the tool's simplicity (1 optional param, no output schema), the description fully covers purpose, use cases, return values (top tools/packs/call volume), data source, and caching. No gaps found.

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%, with the 'window' parameter already well-documented (enum values, default, and behavioral difference between short/long windows). The description does not add new semantic information 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 states explicitly that the tool returns top tools, top packs, and total call volume over a recent window, distinguishing it from siblings like 'discover_tools' by focusing on trending based on call volume.

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?

Three specific use cases are provided, guiding the agent on when to use this tool (e.g., discovering hot data sources, confirming popular tools). However, it does not explicitly contrast with alternatives or state when not to use it.

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 behavior. The description adds rich context: it explains call failure with no args, the semantic anchor (Jaccard similarity ≥0.30), partition filter (placeholder removal), fill check against live CLOB depth, and the output 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 well-structured with sections (REQUIRES, event mode, topic mode, etc.) and front-loads key information. While it is comprehensive and each sentence adds value, it is relatively long and could be slightly more 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 no output schema, the description thoroughly covers response fields (opportunities array, partition_check object, fill check details), edge cases (placeholders, low similarity), and references sibling tool for custom sizing. Everything an agent needs to use the tool effectively is present.

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%, providing a baseline of 3. The description significantly adds value by explaining the difference between modes, accepting full Polymarket URLs, and providing concrete examples. This extra context elevates the score above baseline.

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 finds arbitrage opportunities on Polymarket using monotonicity violations and partition-sum checks. It specifies two modes (event and topic) and distinguishes itself from siblings like polymarket_edges and polymarket_fill_risk by detailing its unique approach.

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 requires one of `event` or `topic`, recommends event for specific markets and topic for cross-event scanning, and advises using polymarket_fill_risk for custom sizing. It provides clear context for when to use each mode, though it doesn't list explicit exclusions.

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 extensively discloses behavioral traits: caching (1h KV-level), model families, edge calculation details (slippage, Kelly fractions), and the _diagnostics section for why segments might be empty. This supplements the readOnlyHint, openWorldHint, and idempotentHint annotations without contradiction.

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?

While front-loaded with purpose, the description is verbose (600+ words) and includes highly detailed model family explanations. It is comprehensive but not concise; an AI agent might struggle with token limits. A more structured format (e.g., bullet points) could improve efficiency.

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 thoroughly explains response structure (by_segment, fed_candidates, _diagnostics), model families, and edge metrics. It covers all necessary context for an agent to interpret results and understand why segments may be empty.

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?

With 100% schema coverage, the description adds substantial meaning: defaults, max limits, usage context, and interactions (e.g., min_kelly vs min_partition_leg_kelly). Each parameter is explained beyond the schema, including tradeable-edge knobs and their rationale.

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 scans Polymarket markets and returns opportunities where Pipeworx data disagrees with market price, specifying three response segments. The verb 'scan' and explicit purpose 'what should I bet on today' distinguish it from siblings like polymarket_arbitrage and polymarket_kalshi_spread.

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 is built for discovering opportunities without paging markets and provides detailed knobs (min_liquidity, max_spread_pp, etc.) to filter tradeable edges. However, it lacks explicit when-not-to-use guidance or direct comparisons to sibling tools, though the context implies its niche.

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, openWorldHint, idempotentHint, destructiveHint. The description adds valuable behavioral context: it explains the snapshot-based nature, the meaning of negative edge_pp_net, and the limitation of 60-day TTL. 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 detailed and well-structured, front-loading the core question. However, it is somewhat verbose, especially the RESPONSE section which could be more concise. Every sentence adds information, but the length may hinder quick scanning.

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 complexity of edge time-series analysis, the description covers key aspects: tracked, expired, snapshot_dates, decay trends, and limitations. However, it lacks precise definitions for 'edge_pp_net' and the decay computation formula. Since no output schema exists, the description bears full burden, but it still provides substantial context.

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 (100% coverage). The description repeats default values and adds the max 30 clamp and default window, but these are already in the schema. The description's extra details about snapshot families are useful but do not significantly add 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's purpose: 'Edge persistence and decay telemetry built from daily polymarket_edges snapshots.' It answers the exact question it addresses ('how long has this edge existed and is it shrinking?') and distinguishes it from sibling tools like polymarket_edges by focusing on time-series and decay analysis.

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 usage context (e.g., 'a fresh wide edge and a 3-week-old wide edge are different trades') and mentions limitations (history depth, snapshot TTL). However, it does not explicitly state when not to use this tool or compare it to alternatives, though the context makes it clear this is for persistence/decay analysis.

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, idempotentHint, and destructiveHint=false, with no contradiction. The description adds behavioral details: returns verdict (clean|degraded|cannot_fill), lists specific return fields, and explains the risk of partial fills leading to unhedged positions.

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 clear sections for modes and usage guidance. While slightly verbose, every sentence adds necessary context. It front-loads the core purpose and ends with a practical warning, making it 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 no output schema, the description thoroughly details return values for both modes (top_of_book, vwap_fill_price, slippage_pp, shares_filled, etc.). It also explains the logic behind basket mode and the forced_directional_risk leg naming. Complete for the tool's 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?

Schema coverage is 100%, so baseline is 3. The description adds meaning beyond schema by explaining the difference between single-market and basket modes, default behavior for side in basket mode, and how size_usd is interpreted differently for buys vs sells and basket vs single-market.

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 the tool performs a 'Realizable-vs-theoretical edge check against live CLOB order-book depth,' with specific verbs and resource. It clearly distinguishes between single-market and basket modes, and the name 'fill_risk' plus context signals show differentiation from siblings like polymarket_arbitrage and 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?

Explicitly states when to use: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' Additionally, explains why not to skip it—theoretical overround is not capturable on thin books, and partial baskets convert arb into directional position, which is the dominant loss mode.

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 adds significant value beyond annotations by detailing safety fields (compatibility_warning, temporal_alignment, skipped counters) and explaining conditions under which spreads are meaningful. Annotations already indicate read-only, idempotent, and non-destructive behavior, and the description aligns and complements them.

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 relatively long but front-loaded with purpose, modes, response, and safety—every sentence adds value. Minor room for structuring, but overall efficient for the complexity.

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?

Without an output schema, the description thoroughly covers the response format (leg-by-leg prices, matched spreads in percentage points) and safety fields. It provides sufficient detail for an agent to understand what the tool returns.

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 all three parameters described. The description adds context: lists the ten topic shortcuts, explains that kalshi_event_ticker overrides topic, and provides examples. This meaningfully supplements 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 computes cross-venue spreads between Kalshi and Polymarket for the same resolving question, specifying the verb 'cross-venue spread' and resource. It distinguishes from sibling tools like polymarket_arbitrage and polymarket_edges, which focus on single-venue arbitrage.

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 explains two modes (topic shortcuts and explicit tickers) and warns that most pre-mapped topics currently return compatibility warnings, guiding users on when not to expect tradeable spreads. It lacks direct comparison to alternatives but provides clear context for usage.

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 indicate readOnly and idempotent behavior. The description adds context about scoping to identifier and the ability to list all keys, enhancing 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 very concise with three sentences that each add value: action, use case, and scoping/pairing. 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?

For a simple tool with one optional parameter, no output schema, and good annotations, the description fully covers functionality, use cases, and relationships to siblings.

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 reinforces the parameter's role but adds no new semantic detail 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 retrieves a saved value or lists all keys, with specific verb and resource, and distinguishes it from sibling tools remember and forget.

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, such as looking up user context, and mentions scoping, but does not explicitly state when not to use it.

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, openWorldHint, idempotentHint, and destructiveHint. The description adds important behavioral details: what each returned event contains (source, citation_uri, raw payload), the effect of setting mark_read true, and that polls work fine. 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 three sentences long, front-loaded with the core purpose, and every sentence adds value. No redundancy or fluff.

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 explains the structure of returned events (source, citation_uri, raw payload). It covers all 5 optional parameters, their effects, and even provides an alternative endpoint. This is more than adequate for a tool with optional parameters.

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 goes beyond schema descriptions by explaining the semantics of 'type' (e.g., 'sec_8k'), 'since' as ISO timestamp, 'mark_read' flags events read for future calls, and 'unread_only'. This adds significant meaning.

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 specific verbs and nouns: 'Pull fired events from your subscription feed' and 'Returns the most recent alerts'. It clearly identifies the resource (subscription feed) and scope (most recent). While sibling tools like list_subscriptions are not directly compared, the description is distinct enough that an agent can differentiate it from other tools.

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 guidance on when to use the tool (e.g., polling, filtering by type/since), mentions an alternative access method (GET endpoint), and explains the effect of mark_read. It does not explicitly state when not to use it or compare to specific siblings, but the context is clear.

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 (readOnlyHint, idempotentHint, destructiveHint) are complemented by detailed behavior: fans out to multiple sources, GDELT preferred with GNews fallback, PatentsView API sunset soft-fail, and relative date shorthand. 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?

Description is a single dense paragraph that efficiently packs examples, source details, and fallback info. Could be slightly more concise, but each 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?

Despite no output schema, description explains return structure (changes[], total_changes, citation URIs) and acknowledges soft-failure for patents. Covers all necessary context for a tool of this 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?

Schema coverage is 100%, and description adds meaningful context: type restricted to 'company', since accepts ISO date or relative shorthand with examples, value can be ticker or CIK. This goes beyond 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?

Description clearly states it provides a change feed for a company over a window, aggregating SEC filings, news (GDELT/GNews), and patents. Distinguishes from sibling tool entity_profile by contrasting dynamic vs static data.

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 query examples ('What's new with X', etc.) and directs use of entity_profile for static profiles. Mentions fallback behavior (GDELT→GNews) but lacks explicit when-not-to-use scenarios.

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?

Provides important behavioral context beyond annotations: key-value scope, session persistence differences for authenticated vs anonymous users, and 24-hour retention. Annotations already indicate non-destructive and idempotent.

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, each adding value. Front-loaded with purpose. 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?

For a simple 2-param tool with no output schema, the description fully explains purpose, usage, persistence, and context. Complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline 3. Description adds naming convention examples (e.g., subject_property) which is helpful but not critical. No new parameter constraints.

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 saves data for reuse across conversations, with specific examples like ticker, address, preference. It distinguishes from siblings recall and forget.

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 to use when discovering something worth carrying forward, and mentions pairing with recall/forget. No explicit when-not, but context is clear.

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, openWorldHint, idempotentHint, and destructiveHint. The description adds that each call cascades through several lookup endpoints internally and replaces 2-3 manual lookups, providing behavioral context 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 well-structured, starting with example queries, then usage instruction, then details per type. It is front-loaded with key information and every sentence adds value, though it is slightly longer than necessary.

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 compensates by explaining exactly what each entity type returns. The tool has only two parameters, and the description covers the input formats and result contents sufficiently for an agent to use it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description adds significant meaning by specifying what 'value' accepts for each entity type (e.g., ticker, CIK, or name for company; brand or generic for drug) and detailing the output structure for each type. This goes beyond the schema's minimal 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: resolving a name to a canonical identifier. It provides example queries like 'what's the ticker for…' and specifies the supported types (company, drug) with what each returns, effectively distinguishing it from sibling tools.

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 says 'Use FIRST whenever you have a name but need an ID,' providing clear guidance on when to use it. It does not mention exclusions or alternatives, but the context strongly implies that if an ID is already known, this tool is unnecessary.

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, openWorldHint, idempotentHint, and destructiveHint false. The description adds behavioral details: it probes each entity with ai_visibility_check, ranks, and returns a ranked list with score, confidence, and signal density per entity. No contradiction.

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 concise sentences plus an example question in parentheses. Every part adds value, with no wasted words. Front-loaded with the core 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?

Despite no output schema, the description clearly states the return format: ranked list with score, confidence, and signal density. It also explains internal use of ai_visibility_check. This is complete for a tool with this complexity and annotation support.

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. The description adds extra meaning beyond schema: first entity is treated as subject for narrative, others as competitors, shared context applied to all probes, and optional models key. This provides semantic enrichment.

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 AI visibility across multiple entities side-by-side, using ai_visibility_check, and ranks by score. It distinguishes from the sibling ai_visibility_check (single probe) by emphasizing the multi-entity comparison aspect.

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 context for competitive AI-marketing audits, including an example question. It implies when to use (multi-entity comparison) but does not explicitly state when to avoid or mention specific alternatives, though sibling context makes alternatives clear.

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 adds significant behavioral context beyond the annotations (readOnlyHint, idempotentHint, etc.). It explains the fan-out across two sources, graceful degradation on partial failures, and the 5-30s delay for first bundlephobia measurement, which are critical for the agent to understand behavior.

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 and concise, front-loading the core purpose in the first sentence, then providing usage, return fields, ecosystem limitation, and error handling. Every sentence adds value 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?

With no output schema, the description fully compensates by listing all return fields (summary block, per-advisory detail, links, alternative versions) and explaining partial failure behavior. The parameter descriptions are sufficient, and the tool's complexity is well covered.

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 parameter descriptions already including defaults and scoped package acceptance. The description restates the same information without adding new semantic details, so it meets the baseline but does not elevate beyond it.

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 is a composite check for npm packages, combining deps.dev and bundlephobia data. It specifies the verb 'scan' and the resource 'dependency', and uniquely identifies itself among siblings by focusing on npm package evaluation.

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 usage guidance: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me".' It also notes the NPM-only limitation in v1 and directs other ecosystems to a different tool, offering clear when-to-use and when-not-to-use advice.

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 mark the tool as read-only, idempotent, and non-destructive. The description adds essential behavioral details: uses BGE-base-en embeddings with cosine similarity over 500-char overlapping windows, a 200K char cap with truncation/flagging, and that passages include offsets for verification. This goes well beyond annotation coverage.

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 of four sentences. The main purpose is front-loaded ('Semantic search INSIDE a fetched record'), followed by input/output summary, usage guidance, and technical specifics. Every sentence adds unique value with no redundancy or fluff.

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 moderate complexity (3 parameters, no output schema), the description covers all necessary aspects: purpose, usage scenario, behavioral mechanics, technical constraints (200K cap, windowing), and return format (passages with offsets and scores). No gaps remain for an agent to guess.

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 bar is higher. The description does not add significant new parameter-level information beyond what the schema already provides. It provides context for the query (natural-language examples) and notes text is 'already pulled', but the schema descriptions are already sufficiently detailed.

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: 'Semantic search INSIDE a fetched record.' It specifies inputs (text already pulled, natural-language query) and outputs (top-N passages with character offsets and similarity scores). This directly and uniquely identifies the tool's role among 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?

Explicit usage guidance: 'Use when the record is too big to cram into the prompt' and mentions a complementary tool (ask_pipeworx_grounded) for grounding. This clearly tells the agent when and how to apply the tool, and implies when not to use it.

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 (write, open world, idempotent, non-destructive), the description adds important behavioral details: OAuth requirement, always-on feed, email/SMS caps (10/day), webhook auto-disable after 10 failures, and verification steps. 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?

Description is well-organized with front-loaded core purpose. It is somewhat lengthy but justifies length with detailed type and delivery explanations. Minor redundancy could be trimmed, but overall efficient.

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 subscription types, parameters, delivery channels, and OAuth requirement. Lacks explanation of idempotency behavior (whether duplicate subscription returns existing or errors) and full response format beyond ID. Given no output schema, slightly incomplete but adequate.

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%, yet description adds significant value: explains enum options with examples for type, provides structure examples for params, and details delivery constraints (verified phone, HMAC signing). Greatly enhances schema information.

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 creates a proactive monitoring subscription to a live-data event stream and returns a subscription ID. It distinguishes from sibling tools like list_subscriptions and unsubscribe by focusing on creation.

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 context on when to use, including supported types and required OAuth account. It implicitly suggests not using with anonymous/BYO accounts, but lacks explicit alternatives like checking existing subscriptions via list_subscriptions.

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 read-only, idempotent, and non-destructive hints. Description adds functional behavior: returns categorized examples from a live catalog, with exact tool calls. 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?

Contains a list of aliases upfront, then a clear description. Every sentence is informative, though slightly verbose. Good front-loading of 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?

Given the tool's simplicity (one optional parameter, no output schema), the description is very complete: it explains the return format, usage context, and parameter 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 coverage is 100% with a description for the 'topic' parameter. Description adds specific enum values (finance, pharma, etc.) and clarifies behavior when omitted, adding 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?

Description explicitly states the tool returns example questions with exact tool+argument shapes, and distinguishes it as the onboarding entry point. It clearly separates from sibling tools by specifying when to use it first.

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 this FIRST' when the agent doesn't know what Pipeworx can do, and explains how to use it with or without the topic parameter. Implies alternative tools for specific tasks.

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?

Adds significant behavioral details beyond annotations: ownership enforcement, deactivation (not deletion), historical events preserved via recent_alerts. Annotations only provide hints; description fills in specifics.

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 with no wasted words. Every sentence adds value: purpose, constraints, side effects.

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?

Complete for a single-parameter tool with no output schema. Covers purpose, constraints, side effects, and relationship to sibling tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Only one parameter 'id' with schema description already explaining it's a UUID returned by subscribe. Description does not add extra meaning; schema coverage is 100%, baseline score 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 clearly states 'Cancel a subscription by id', using a specific verb and resource. It distinguishes itself from sibling tools like 'subscribe' and 'list_subscriptions'.

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 context: ownership enforcement and deactivation behavior. Lacks explicit when-to-use vs alternatives, but implied by purpose.

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 indicate read-only, idempotent, and open-world behavior. The description adds beyond annotations by detailing the return type (verdict with categories, extracted structured form, actual value with citation, percent delta), the data sources (SEC EDGAR + XBRL), and the efficiency improvement over multiple sequential calls. 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 compact (three sentences) and front-loaded with example queries. Every sentence serves a purpose: establishing use cases, specifying scope, and highlighting benefits. It could be slightly more structured, but it is efficient and avoids 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?

Given the tool's complexity (claim verification), no output schema, and rich annotations, the description covers domain, input format, data sources, return types, and efficiency. It lacks details on error handling or rate limits, but the annotations and parameter documentation fill some gaps. Overall, it is sufficiently complete for an agent to use correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% for the single parameter 'claim', so baseline is 3. The description adds value by providing concrete examples of natural-language claims (e.g., 'Apple's FY2024 revenue was $400 billion'), which helps the agent understand the expected format 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 the tool's purpose: factual claim verification against authoritative sources, specifically company-financial claims. It uses specific verbs like 'validate,' 'verify,' and 'confirm or refute,' and it distinguishes itself from siblings by focusing on structured financial data via SEC EDGAR + XBRL.

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 says 'Use whenever the agent needs to check whether something a user said is factually correct' and scopes to 'company-financial claims (revenue, net income, cash position for public US companies).' It implies alternatives by stating it replaces 4-6 sequential calls, making the usage context clear.

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