TensorFeed

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

No annotations are provided, so the description bears full responsibility for disclosing behavior. It covers: input formats (wallet or token_prefix), output fields (composite, sub-metrics, trust grade, flags, wallet age, etc.), rebuild schedule (daily at 04:50 UTC), data source (TF's own telemetry), and error handling (ok=false for unknown). All critical behavioral aspects are transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph of about 120 words, front-loaded with the primary purpose. Every sentence adds value: input constraints, output details, schedule, error handling, and use cases. There is no wasted text, and the structure is logical and easy to follow.

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 absence of an output schema and annotations, the description comprehensively covers all necessary context: input requirements, output structure (listing all major components), data freshness, error behavior, and practical use cases. It leaves no critical gaps for an agent to select and invoke 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?

Since the input schema already describes both parameters with 100% coverage (wallet: 'EIP-55 or lowercased EOA wallet address (0x + 40 hex). Mutually exclusive with token_prefix.'; token_prefix: 'First 16 chars of a tf_live_ bearer token (e.g. "tf_live_18e54f47"). Mutually exclusive with wallet.'), the tool description adds minimal additional semantic value beyond restating these details. 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 tool looks up an agent's public reputation card from the TensorFeed Agent Reputation Bureau. It specifies the input types (wallet or token_prefix) and the output structure in detail. Although it does not explicitly compare to sibling tools, its unique function (agent reputation lookup) is distinct from all listed siblings (e.g., check_ai_supply_chain_risk, get_agent_opportunities), making the purpose unambiguous.

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 use cases: marketplaces routing work, peer agents deciding trust, ops dashboards, and operators inspecting their own reputation. It also clarifies the error handling (ok=false for unknown identities), which helps callers handle failures. However, it does not explicitly state when not to use the tool or mention alternative tools, which would strengthen the 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?

Describes return content (publicly-disclosed malicious packages), matching behavior (case-insensitive substring), and source attribution (GHSA). No annotations provided, but description adequately covers behavioral traits for a read-only tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured and informative, front-loading purpose. Could be slightly more concise, but 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?

No output schema, and description does not detail the fields of returned entries (only mentions GHSA source). Adequate for a simple snapshot tool, but could be more 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 coverage is 100%, so baseline is 3. Description adds context: ecosystem filter, case-insensitive substring, and behavior when omitted (returns all). Adds usage hint ('before installing').

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it checks a feed for malicious AI-related packages, specifies the feed name (TensorFeed) and ecosystems (npm/PyPI). Distinct from sibling tools like check_agent_reputation.

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?

Explains when to use with no args (whole snapshot) vs with package_name (check specific package before installing). Lacks explicit when-not or alternative comparisons, 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?

Describes exactly what is returned (used_today, remaining, resets_at) and confirms no quota is consumed. Also states 'no auth required', which is critical behavior. With no annotations, the description fully covers behavioral expectations.

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 that front-load the purpose and add no filler. Every sentence contributes meaningful 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 no output schema, the description lists all return fields and key behaviors (auth, quota consumption, rolling window). It is complete for the tool's simple function.

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?

There are zero parameters, so schema coverage is 100%. The description adds value by explaining the output fields and quota consumption behavior, which goes beyond the empty 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 specifies the tool checks the caller IP's free premium-API trial quota, using the specific verb 'Check' and naming the exact resource. It stands out from sibling tools like check_agent_reputation or get_models, which are unrelated.

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 advises using the tool before deciding between a paid call or waiting for reset, providing clear context. It also notes the tool consumes no quota, which helps in decision-making. No alternatives are listed, but the tool is singular in 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?

Despite no annotations, the description fully discloses behavioral traits: refresh schedule ('Refreshed daily at 13:30 UTC'), data source ('GitHub data via the public Search API'), licensing, and the composite score formula. It also implies read-only behavior without explicit 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 moderately concise (several sentences) but each sentence contributes essential information (frequency, output fields, use cases, license). It could be slightly trimmed without losing clarity, but it remains 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?

Given no output schema, the description fully explains the return fields (GitHub repo path, description, stars, last update, source signal, composite score) and the scoring formula. It also covers data source and refresh schedule, leaving no major 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 both parameters described. The description adds value by enumerating valid signal values (e.g., 'anthropic-org', 'openai-org') and explaining how the 'limit' parameter caps results (1-25). This goes beyond the schema's minimal 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 returns a daily scan of new repositories across the AI agent ecosystem. It uses specific verbs ('Get') and identifies the resource ('TensorFeed's daily scan'). The description distinguishes this from sibling tools like 'get_hf_daily_papers' or 'get_models' by focusing on opportunities with composite scores.

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 use cases: 'surfacing distribution targets, integration ideas, or just a daily digest'. However, it does not explicitly state when not to use this tool or mention alternative tools for related 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?

No annotations are provided, so the description carries the full burden. It discloses that results are from five fan-out queries, deduped, refreshed daily, and licensed under Semantic Scholar API with attribution. While it doesn't mention rate limits or error behavior, the given details are comprehensive for a read-only tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (about 75 words), well-structured, and front-loaded with the core purpose. Every sentence adds meaningful information without 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 low complexity (single optional parameter, no output schema, no nested objects) and no annotations, the description covers all essential aspects: source, methodology, ranking, filtering, refresh schedule, license, and comparison with a sibling tool. Nothing critical is missing.

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 only one parameter (limit) and 100% schema coverage, the baseline is 3. The description adds value by explaining how limit interacts with the tool's internal logic (fan-out and dedup producing top 30), providing context beyond the schema's simple range and default description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it retrieves daily curated AI/ML trending papers from Semantic Scholar, ranked by citation count. It mentions the five fan-out queries, deduplication, and a top-30 limit, which precisely defines the tool's scope. It also distinguishes itself from the sibling tool get_arxiv_recent by describing the ranking method.

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 positions the tool relative to get_arxiv_recent (citation-ranked vs. firehose by submission date), giving clear guidance on when to use each. The description includes refresh timing (daily at 11:00 UTC) and license restrictions. Lacks explicit 'do not use when' scenarios, but the comparison 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?

With no annotations, description carries full burden. It discloses refresh schedule, field list, sorting, and license/attribution. No contradictions or omissions.

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 concise (~80 words), front-loaded with core purpose, and logically structured: purpose, fields, schedule, sibling reference, license. 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?

Low complexity (1 param, no output schema) but description covers all: purpose, scope, fields, refresh, sibling, license. No additional information needed for correct invocation.

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

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

Only parameter 'limit' has 100% schema coverage with clear description. Description adds no extra meaning beyond schema, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description specifies verb 'Get', resource 'arXiv submissions', scope '50 most recent in cs.AI/cs.LG/cs.CL/cs.CV', and explicitly differentiates from sibling get_ai_trending_papers.

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 when to use (daily refresh at 11:30 UTC) and explicit alternative (use get_ai_trending_papers for citation ranking), guiding agent selection.

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

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

No annotations provided, so description carries full burden. Discloses lazy-fetching, 7-day caching, license terms, and attribution requirement, which are helpful behavioral details.

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 succinct, front-loaded sentences with no wasted words. Every sentence provides essential information: purpose, example, caching, license, and attribution.

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 lookup with one parameter and no output schema, the description adequately covers purpose, caching, and license. It lacks detail about the response format but is otherwise 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 coverage is 100% with a clear description of the cve_id parameter. The tool description adds an example and mentions case-insensitivity, but this closely mirrors the schema, adding no significant new 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?

Description clearly states the action ('look up'), specific resource ('CVE Record v5.2 from the MITRE CVE List'), and provides an example ID. It distinguishes from siblings like get_kev_catalog and get_epss_score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Implied usage for retrieving a single CVE by ID, but no explicit guidance on when to use this vs. alternatives or prerequisites. Adequate for a simple lookup.

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

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

No annotations are provided, so the description must disclose behavioral traits. It does not mention read-only nature, authentication requirements, rate limits, or any side effects. The only behavioral clue is the license note, which is insufficient for full 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 three sentences: purpose, curated routes, license. It is front-loaded and contains no redundant or irrelevant information. 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?

The description adequately covers the tool's function and parameter context, but since there is no output schema, it would benefit from explaining the return format (e.g., time-series structure). For a 4-parameter tool with 100% schema coverage, this is adequate but not 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?

The schema covers all parameters (100%), but the description adds value by grouping routes into meaningful categories (e.g., 'petroleum/pri/spt (WTI/Brent crude spot prices)') and noting the data license. This enriches understanding beyond the minimal schema 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 retrieves US Energy Information Administration time-series data, and lists specific curated routes (e.g., petroleum/pri/spt, electricity/retail-sales), making the purpose highly specific and distinct 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 provides context on the intended use case (US energy data) and lists curated routes, but does not explicitly state when to use this tool over alternatives or when not to use it. Usage is implied through the domain specificity.

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

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

No annotations provided, so description carries burden. It discloses source and license but does not explicitly state that it is read-only or mention any authentication or rate limits. The return values are described, but behavioral traits like non-destructiveness are implied.

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, no fluff. Each sentence adds value: first states purpose and source, second describes return and license.

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 parameter and no output schema, the description adequately covers purpose, return values, and data source. No missing critical 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 coverage is 100% with a clear description of cve_id. Description adds no extra meaning beyond the schema, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description uses specific verb 'Get' and identifies the resource as 'EPSS probability for one CVE', sourced from FIRST.org. This clearly distinguishes from siblings like get_cve_record or get_kev_catalog.

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?

Description states what the tool returns (probability and percentile rank) and implies usage when exploit prediction score is needed. No explicit when-not-to-use or alternatives, 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?

No annotations exist, so description carries full burden. Describes data fields and refresh time, but omits behavioral traits like authentication needs, rate limits, error cases, or side effects. Adequate but not rich.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Concise two-sentence description that front-loads purpose and quickly lists fields. Efficient with no wasted words, though slightly more structure (e.g., bulleted fields) could improve readability.

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 simplicity (1 param, no output schema, no annotations), the description is fairly complete: lists all fields, states refresh time, and differentiates siblings. Missing error/authentication details, but acceptable for such a 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?

Only one parameter (limit) with 100% schema coverage. The description adds no additional meaning beyond what the schema already provides. 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?

Uses specific verb 'Get' and resource 'Hugging Face's editor-curated daily AI/ML papers feed'. Clearly lists returned fields and differentiates from two sibling tools: get_arxiv_recent (firehose) and get_ai_trending_papers (citation-ranked).

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 for when to use this tool versus alternatives by contrasting signal types with siblings. Lacks explicit when-not-to-use or exclusions, but the differentiation is clear enough for an AI agent.

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

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

With no annotations provided, the description fully carries the burden. It discloses that the tool is read-only (getting catalog), non-destructive, publicly available, and has a defined update frequency. Mentions approximate catalog size ('~1500 actively-exploited CVEs') and license, giving the agent a clear understanding of behavioral traits.

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, with three sentences that are front-loaded with the main purpose. Each sentence provides unique value: purpose, behavior (returns N entries + metadata), schedule, license, and scope. 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 low complexity (one parameter, no output schema), the description is complete. It covers what the tool does, what it returns, when it is updated, and the data's public domain nature. The agent has enough information 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 input schema has 100% coverage for the only parameter (limit) with a clear description ('Top-N most-recent entries (1-50)'). The tool description does not add any additional meaning beyond what the schema provides, so the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states 'Get the CISA Known Exploited Vulnerabilities (KEV) catalog' with a specific verb and resource. The description distinguishes it from sibling tools like get_cve_record and get_epss_score by focusing on the KEV catalog. It also specifies what is returned (most recent N entries and catalog metadata).

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 on when to use (to get recent entries) and includes refresh schedule ('Refreshed daily at 06:30 UTC') and license ('US Government public domain'). It implicitly differentiates from siblings by focusing on actively exploited CVEs, but does not explicitly state when not to use or suggest alternatives.

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

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

No annotations are provided, so the description carries the full burden. It discloses the daily refresh frequency, which adds context, but does not mention authentication, rate limits, or error behavior. For a read-only catalog, this is adequate but not thorough.

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: the first states the core function, the second adds specifics and update frequency. No wasted words, and the key information is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has no parameters, no output schema, and simple functionality (retrieving a catalog), the description fully covers what the tool does, what data it includes, and its freshness. No missing critical aspects.

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 zero parameters (100% schema description coverage), so the baseline is 4. The description adds meaning by detailing the contents (per-token pricing, context windows, capabilities, deprecation flags), which goes beyond the empty schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Get' and the resource 'model pricing and specs catalog across providers'. It lists specific providers and data types, distinguishing it from sibling tools like get_eia_series (energy data) or get_arxiv_recent (papers).

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 when model pricing/specs are needed but provides no explicit guidance on when not to use it or alternatives. No exclusions or comparisons to siblings are mentioned.

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

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

With no annotations, the description carries full burden. It discloses polling every 10 min, deduplication, sanitization for prompt injection, and return fields. This is comprehensive for a read tool, though no mention of rate limits or authorization.

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, front-loaded with the main purpose, and every sentence adds value without unnecessary words. It uses bullet points effectively to convey key features.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple retrieval nature, 2 parameters, and 100% schema coverage, the description is complete. It covers source list, update frequency, data processing, limits, and output schema indirectly, fully meeting the agent's needs.

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

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

Schema coverage is 100% with descriptions provided. The description adds value by giving example categories ('models, business, research') and clarifying the return fields (title, snippet, source, publishedAt), going beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it gets the latest AI news articles from 12+ sources, listing specific providers. It distinguishes from sibling tools like get_ai_trending_papers and get_arxiv_recent by focusing on general AI news aggregation.

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 obtaining AI news but does not explicitly state when to use or not use this tool versus siblings. It provides context (poll frequency, dedup) but lacks direct 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?

No annotations, so description carries full burden. Lists license and identifier prefixes but does not disclose rate limits, error handling for missing IDs, or data freshness. Behavior is mostly predictable for a lookup tool, but lacks depth.

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, zero wasted words. Front-loaded with action and resource. License note provides 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?

No output schema, so description should at least hint at response content (e.g., advisory details). Current description only covers input and license, missing expected output. Good for input side, but incomplete for agent to anticipate return format.

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 for the single 'id' parameter. Description adds value by listing example IDs and documenting the supported prefixes, going 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?

Explicitly states lookup of a single OSV advisory by ID, lists accepted identifier prefixes, and clearly distinguishes from siblings like get_osv_advisory_for_package and get_cve_record.

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?

Purpose is clear but no guidance on when to use this versus get_osv_advisory_for_package or get_cve_record. Does not state when not to use or provide alternatives.

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

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

No annotations provided, so description carries full burden. Only states it returns advisories and the license. No disclosure of rate limits, error handling, auth requirements, or side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three concise sentences with no wasted words. Front-loaded with purpose and ecosystem examples.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema; description does not specify structure of returned advisories (e.g., fields like advisory ID, summary). Tool complexity is low, but completeness is weak for a lookup 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?

Adds value beyond schema by listing example ecosystems and clarifying version as optional. Schema already provides parameter descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it is a cross-ecosystem vulnerability advisory lookup via OSV.dev, lists supported ecosystems, and specifies inputs and outputs. Distinguishes from sibling get_osv_advisory_by_id.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives like get_osv_advisory_by_id. Usage is implied but not formally stated.

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

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

With no annotations, the description discloses important behaviors: refresh rate (every minute), license (public domain), and return format (flattened list with specific fields). It does not mention rate limits, authentication, or error handling, but for a read-only data feed this is adequate.

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 four sentences, each serving a purpose: purpose, parameter selection, return format, and additional info. It is front-loaded with the main action and is free of unnecessary 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 no output schema, the description sufficiently explains the return fields (id, magnitude, place, time, depth, coordinates, tsunami flag, URL). It also covers data source, refresh rate, and licensing. Missing details like error handling are not critical for this simple query tool.

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

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

Schema description coverage is 100%, so the baseline is 3. The description reiterates parameter options (magnitude bucket values, time window values) and implicitly mentions limit via the return format, but adds minimal new meaning beyond the schema's own parameter descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves recent earthquakes from USGS summary feeds, specifying the source and type of data. Among sibling tools like get_weather_alerts or get_news_articles, this is uniquely about earthquakes, so purpose is unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains how to choose magnitude and time window parameters, providing explicit options. However, it does not discuss when to avoid this tool or compare it to alternatives; given the sibling tools are unrelated, usage is clear but lacks 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?

With no annotations, the description carries the full burden. It discloses the return content (profile fields and ~1000 most recent filings) and the license. It does not mention rate limits, authentication, or potential errors (e.g., invalid CIK), but it provides a clear picture of what the tool does and returns.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, each serving a distinct purpose: action, input format, output details, and license. It is front-loaded with the key verb and resource, and no sentence is redundant or unnecessary.

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 adequately describes the return values (profile fields and filings count). For a single-parameter tool, the description covers essential aspects. However, it lacks details like filing date range or ordering, which would be beneficial for completeness.

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

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

The schema has 100% description coverage with a brief param description. The tool description adds significant value by enumerating three accepted CIK formats with examples, which clarifies the parameter's flexibility 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 action ('Get entity metadata + recent filings for one company by CIK'), specifies the resource (SEC submissions), and distinguishes itself from siblings like `search_sec_edgar` by focusing on a single company lookup. It also clarifies the acceptable CIK formats, making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use this tool (for a single company's metadata and filings). It does not explicitly mention alternatives or when not to use it, but the context from sibling names (e.g., `lookup_sec_company_ticker`, `search_sec_edgar`) suggests other tools for different use cases. The license info adds minor usage context.

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 polling frequency (every 2 min) and output format (operational|degraded|down per service plus most recent incident). No annotations provided, so description carries burden well.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with front-loaded purpose and list of services; 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?

Covers purpose, output, and update frequency. Lacks error handling or auth details but sufficient for a simple read-only status check.

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?

No parameters exist (0 params), so baseline 4 applies. Description does not need to add parameter detail.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states verb 'Get' and resource 'live operational status of every major AI service tracked by TensorFeed' with explicit examples. Distinct from sibling tools like check_agent_reputation.

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?

Does not explicitly state when to use vs alternatives, but the purpose is self-evident and no close sibling tools exist, so missing exclusion guidance is less critical.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It correctly implies a read-only lookup, but does not detail potential side effects, rate limits, or permissions. The simplicity of the operation partially mitigates this, but the description lacks explicit behavioral traits.

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, highly concise, with the primary purpose front-loaded. Every sentence adds value, and the description is well-structured for quick agent comprehension.

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 parameter, simple boolean/optional date return), the description is nearly complete. It describes the return shape, though no output schema exists. A slight gap is the lack of behavioral details, but overall it covers the essential 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 description coverage is 100%, fully documenting the 'wallet' parameter with format details (EIP-55 or lowercased, 0x+40 hex). The tool description does not add further parameter context, so a baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool checks for an active 'verified-hireable' subscription on the TensorFeed Agent Directory, specifying the exact boolean and optional ISO date return. The verb 'look up whether' and resource 'wallet subscription status' are specific, distinguishing it from sibling tools like check_agent_reputation.

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 the tool is useful for 'downstream marketplaces or peer agents who want to confirm...before routing work,' providing clear context. It does not explicitly state when not to use or list alternatives, but the sibling tool list implies these exist.

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

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

No annotations provided, so the description carries full responsibility. It discloses real-time refresh, 60-second caching, public domain license, and the returned fields. It does not mention authentication or rate limits, but for a read-only public API this is sufficient.

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 that efficiently conveys purpose, filters, return fields, caching behavior, and license. Every sentence adds value; 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?

Despite lacking an output schema, the description lists return fields. All 6 parameters are explained. Caching and licensing are noted. For a read-only query tool, the description provides sufficient context for correct invocation.

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

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

Schema coverage is 100% and each parameter is described in the schema. The description adds value by enumerating accepted values for severity, urgency, and status, and clarifies the event parameter expects exact NWS event names. This provides context 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?

Clearly states the tool returns active US weather alerts from the National Weather Service, specifying the resource (weather alerts) and the source (NWS). The description distinguishes it from sibling tools because no other sibling focuses on weather alerts.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not provide explicit guidance on when to use this tool versus alternatives. It lists filters but lacks context on selection criteria or exclusions. No alternatives are mentioned.

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

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

No annotations are provided, so the description carries the full burden. It mentions that the tool acts on 'all active operator claims' and is sorted by count descending, implying a read-only operation. However, it does not explicitly disclose whether it is destructive, requires authentication, or other behavioral traits. The description adds some context but not comprehensive 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 extremely concise: two sentences, front-loaded with the core action, and a second sentence providing use cases. Every sentence adds value, no 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 zero parameters and no output schema, the description adequately conveys what the tool returns and its purpose. It specifies 'distribution of skill tags', 'sorted by count desc', and 'across all active operator claims'. However, it does not describe the output format (e.g., JSON structure), which would be helpful for an agent. It is mostly complete for a simple tool.

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

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

The tool has zero parameters and the input schema is empty, so schema coverage is 100% by default. The description does not need to explain parameter semantics. Per guidelines, a baseline of 4 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns a distribution of skill tags across all active operator claims, sorted by count descending. It provides explicit use cases for agents discovering work types and for agent-marketplaces mirroring controlled vocabulary. This distinguishes it from sibling tools like 'search_agent_directory'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains when to use the tool (for discovering work types or mirroring controlled vocab), but does not explicitly state when not to use it or provide alternatives. The context is clear, but exclusions are missing.

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

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

Annotations are absent, so the description must fully disclose behavioral traits. It only states what the tool returns and its license, but fails to mention idempotency, error handling, authentication requirements, rate limits, or any side effects. The agent cannot assess safety or failure modes from this description alone.

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, consisting of two meaningful sentences plus a license note. It front-loads the purpose and return fields, and every sentence contributes value. The license note is minor but not detrimental.

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 low complexity (single parameter, no output schema, no annotations), the description covers the essential usage: purpose, return fields, and a pairing hint. However, it lacks details on error responses or edge cases (e.g., invalid ticker), which would improve completeness.

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

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

The input schema covers 100% of parameters with a description for 'ticker_or_cik'. The description adds value by providing examples (AAPL, MSFT, NVDA) and clarifying that the input is case-insensitive, which goes beyond the schema's basic 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 verb 'Resolve' and the resource 'stock ticker symbol or numeric CIK to canonical SEC entity record'. It provides concrete examples (AAPL, MSFT, NVDA) and lists the return fields. It distinguishes from sibling tools like 'search_sec_edgar' by specifying the exact output and pairing hint with 'get_sec_submissions'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description includes explicit guidance to 'Pair with get_sec_submissions to retrieve filings', which helps agents understand the typical workflow. However, it does not specify when NOT to use this tool or mention alternatives beyond that pairing, so it lacks 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?

No annotations provided, so description carries full burden. It mentions license and permissions but does not disclose behavioral traits like idempotency, rate limits, authentication, or side effects. The read-only nature is implied but not explicit.

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 concise sentences with no redundant information. First sentence states purpose, second lists returns, third gives use case and license. Front-loaded with most important 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?

With no output schema, the description adequately describes return content. It covers the database source, return fields, use case, and license. Missing details about pagination or search syntax but those are covered in schema. Overall fairly 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 parameters are already well-described. The description adds no additional parameter-level information beyond what the schema provides, warranting the baseline score of 3.

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 queries the FDA MAUDE medical device adverse event database, specifies return fields (device identifiers, problem codes, etc.), and distinguishes from sibling tools that query drug or food data. The verb 'Query' and resource 'MAUDE database' are specific.

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 'Useful for safety signal detection on FDA-cleared devices,' providing a clear use case. While it doesn't specify when not to use or name alternatives, the context of sibling tools (query_fda_drug_events, etc.) makes differentiation easy.

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

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

No annotations are provided, so the description carries full responsibility. It notes the openFDA license but fails to mention behavioral traits such as whether the tool is read-only, any authentication requirements, rate limits, or pagination limits beyond the schema. The description does not contradict annotations (none exist).

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 conveys essential information efficiently. It includes the license, which is relevant for usage rights. However, it could be slightly more concise by separating the license into a note.

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 four parameters and no output schema, the description covers the main purpose and search syntax. However, it lacks details on pagination behavior (beyond schema defaults), rate limits, or error handling. The information is adequate but not 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 description coverage is 100%, but the description adds value by explaining the openFDA Lucene-style search syntax and providing an example. This clarifies how to use the 'search' parameter beyond the schema's brief 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 specifies the tool queries the FDA FAERS adverse event reports database and lists the types of data returned (drug-event records, demographics, etc.). It distinguishes itself from sibling tools like query_fda_device_events and query_fda_drug_labels through its specific focus on adverse events.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description mentions the search syntax and license but does not explicitly state when to use this tool over siblings or provide usage constraints. Usage is implied by the tool name and description, but no alternatives or exclusions are given.

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

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

With no annotations, description carries full burden. Only mentions license terms; no disclosure of rate limits, authentication, data freshness, or API behavior beyond the license.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short sentences front-loaded with main purpose and results. No redundant text; efficient and clear.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers core functionality and license. Missing overview of pagination, default behavior, or output structure. Adequate for a simple query tool with well-documented params.

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 all parameters with descriptions (100% coverage). Tool description adds no additional parameter context beyond listing return sections.

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 it queries FDA SPL for drug labels and lists specific sections returned. Clearly distinguishes from sibling FDA drug tools (events, recalls).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this versus sibling tools like query_fda_drug_events or query_fda_drug_recalls. Missing context-specific usage instructions.

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

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

With no annotations, the description should disclose behavioral traits. It mentions the license but omits pagination mechanics, rate limits, read-only nature, or search syntax details beyond what is already in the schema parameter descriptions.

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 are highly concise, front-loaded with the core purpose and output, and include relevant license information without 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 no output schema, the description effectively explains return fields. It lacks mention of error handling or empty results, but for a simple query tool with no required parameters, it is reasonably 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 coverage is 100%, so baseline is 3. The description adds no additional parameter information beyond what the schema already provides for skip, sort, limit, and search.

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 queries the FDA drug recall database and lists specific fields returned, differentiating it from sibling tools querying other FDA datasets.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies use for recall queries via its title and context, but provides no explicit guidance on when to use this tool versus alternatives like query_fda_drug_events or query_fda_food_recalls.

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

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

No annotations provided; description carries full burden. Mentions license but omits critical behavioral traits like read-only safety, rate limits, authentication needs, or pagination details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short sentences plus license info; concise and front-loaded. License statement is secondary but not detrimental.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema; description relies on external knowledge ('Same shape as drug recalls') and does not describe return fields or pagination behavior, leaving agents uncertain about response format.

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%, description adds no extra parameter meaning beyond field types and defaults. Baseline score of 3 is appropriate as description does not enhance parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it queries the FDA food enforcement (recall) database for US food products, distinguishing it from sibling tools like query_fda_drug_recalls. The verb 'query' and resource are specific.

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?

Implies use for food recalls versus drug recalls via 'Same shape as drug recalls,' but does not explicitly state when to prefer this tool over alternatives or provide 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?

Despite no annotations, the description fully discloses behavioral traits: limits (5 per IP, 25 fires, 30-day TTL), secret generation behavior, and management endpoints. It also notes that the secret cannot be retrieved later.

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 front-loaded with the core purpose and covers multiple aspects efficiently. It is relatively concise given the complexity, though could be slightly tighter.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers watch types, limits, and secret handling well. However, it lacks information about the return value or response format, which is important since there is no 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?

The input schema has 100% coverage with detailed parameter descriptions. The description adds context about IP limits and TTL but does not significantly enhance parameter understanding beyond what the schema already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool registers a free webhook subscription, listing multiple watch types (price drop, status change, etc.). It distinguishes itself from sibling tools, which are mostly read/search tools, by being the only registration tool.

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 free-tier webhook subscriptions and mentions it uses the same delivery infrastructure as paid watches. It does not explicitly state when not to use it, but the context of siblings makes it 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?

With no annotations, the description carries full burden. It discloses cost, payment methods, and that the result is signed. However, it does not cover error handling, return format details, or rate limits, leaving gaps in behavioral understanding.

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 dense paragraph with key info front-loaded (purpose, cost). It could be more structured, but 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?

Given no output schema and no annotations, the description adequately covers purpose, inputs, and cost but lacks details on output format, success/error conditions, and example usage. For a complex paid tool, more completeness would be beneficial.

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

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

Schema description coverage is 100%, so the description adds only marginal value, such as clarifying the payment parameter's role. The baseline of 3 is appropriate as the schema already explains 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 clearly states the tool returns 'the single best model to use right now' for a task or named model. It specifies the verb 'returns' and resource, and distinctively positions itself against siblings which are unrelated data retrieval 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 explains when to use the tool (for task or model routing) and the context of paid calls. It does not explicitly state when not to use or mention alternatives, but the sibling tools are clearly different, so no confusion.

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

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

With no annotations, the description fully discloses key behaviors: free tier capped at 25 results, verified-hireable members sort first, and specific null-handling for max_rate and min_experience filters. It also explains return fields and that TF takes no fee, providing complete transparency for a read-only search tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured: first sentence states purpose, then lists filters, explains special behaviors, and describes return fields. Every sentence adds essential information without redundancy or fluff, making it both concise and informative.

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 (8 parameters, no output schema), the description is highly complete. It explains filter behavior, sorting, result cap, and return fields, and even includes platform context (TF takes no fee). This adequately prepares 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?

All 8 parameters have descriptions in the schema (100% coverage), raising the baseline to 3. The description adds value by explaining behavioral nuances for max_rate (operators with no rate are not filtered out) and min_experience (operators with no value are filtered out), and by noting the controlled vocabulary for skill. This extra context justifies a 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 searches the TensorFeed Agent Self-Directory for hireable AI agents, specifying the verb (Search), resource (Agent Self-Directory), and scope (hireable). It lists numerous filters and return fields, making the purpose distinct from sibling tools like check_agent_reputation or list_directory_skills.

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 on when to use the tool (e.g., finding hireable agents with specific skills, languages, or rates) but does not explicitly mention when NOT to use it or name alternative tools. However, the sibling tools are sufficiently different that implicit differentiation 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?

With no annotations, the description must disclose behavior. It mentions Lucene-style search and public domain license but lacks details on rate limits, pagination, response format, or query constraints. Adequate but not thorough.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, front-loading purpose and form examples. The last sentence is slightly marketing-flavored but still informative. Could be slightly more structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 5 parameters, no output schema, and no annotations, the description covers purpose, data source, and form examples. Missing date range syntax details and response structure, but adequate for a search tool.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by noting Lucene syntax for 'q' and listing example forms, which aids understanding beyond the schema alone.

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 performs full-text search on SEC EDGAR filings since the 1990s, lists specific form types, and targets finance/equity-research agents. It distinguishes itself from siblings like 'get_sec_submissions' (which retrieves specific company submissions) and 'lookup_sec_company_ticker'.

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 financial research but does not explicitly state when not to use or provide comparisons to alternatives. The context from sibling tools helps, but explicit guidelines are absent.

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 anonymity, no PII collection, 30-day expiry, per-IP rate limit (5/24h), and nature as signal collector. No annotations provided, so description carries full burden and does it well.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, front-loaded with main action, no redundant information. Efficient and clear.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers purpose, behavior, constraints, and intent. Could mention expected response or error handling, but not critical for a submission 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 with detailed parameter descriptions. Description adds no extra parameter-level meaning, 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?

First sentence clearly states verb 'submit', resource 'wantlist entry', and purpose 'telling TensorFeed what data you wish was served'. Distinguishes from sibling read-only tools (all 'get_' or 'check_' etc.).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides usage context: 'Aggregated patterns inform TF's pipeline priorities', 'Signal collector, not a contract', and rate limit. No explicit exclusion or alternative mention, but siblings are all read-only, so this is the only write tool.

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?

Without annotations, the description carries the burden. It discloses the tool is paid, requires credits or payment, supports polling for free unchanged briefs, and returns a signed brief. It does not explicitly state it is a read-only operation, but the context implies it. Could be improved by stating safety characteristics.

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 due to necessary complexity around payment and polling, but it is front-loaded with the premium nature. Every sentence adds value. Minor redundancy could be trimmed, but overall well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of output schema, the description mentions what the brief includes (headlines, model pricing deltas, incident detail, AFTA-signed) but not the structure. It covers payment, polling, and distinction from siblings well. Lacks explicit response format, 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%, so baseline is 3. The description adds valuable context beyond the schema, especially for the 'since' parameter (explaining free polling) and 'payment' parameter. This enhances agent understanding of parameter usage.

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 returns the full TensorFeed morning brief with headlines, model pricing deltas, and incident detail. It distinguishes from the free preview sibling. However, it lacks an explicit verb like 'retrieve' or 'get', so purpose is slightly implicit.

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 when to use the paid tool versus the free preview sibling, details the polling mechanism with the since parameter to avoid charges for unchanged briefs, and covers all payment options (x402 wallet, Bearer token, free trial credits). This is comprehensive and actionable.

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