Claidex MCP

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

No annotations are provided, but the description adequately conveys a read-only operation returning static information. It does not contradict any annotations, and the absence of side-effects is reasonable for a capabilities 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 a single, well-structured sentence with no redundant information, efficiently communicating the tool's output.

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 parameters and output schema, the description is sufficiently complete for an agent to understand 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?

With zero parameters and 100% schema coverage, the description adds value by detailing what the tool returns (feature map, providers, etc.), going 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 explicitly states it returns the Claidex MCP feature map, providers, safety controls, resources, prompts, and tool counts, providing a specific verb and resource. It distinguishes itself from sibling tools which are domain-specific (e.g., query, search) by being a meta-tool that returns server capabilities.

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 understanding server capabilities, and since no sibling tool performs the same function, the context is clear. However, it does not explicitly state when to use or provide exclusion criteria.

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

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

No annotations are provided, so the description must cover behavioral traits. It indicates a search-and-summarize function but does not disclose whether it is read-only, any side effects, authentication needs, or rate limits.

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

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

The description is a single sentence with 16 words, no redundancy, and front-loads the action verb 'Search'.

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 two parameters and no output schema or annotations, the description is adequate but could be more complete by specifying output format or any limitations on the summarization.

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 50% (query has description, limit does not). The description adds context by specifying the summarization outputs, but does not explain the 'limit' parameter beyond what the schema provides (default, min, max).

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 claims and summarizes specific risk-related aspects (MRS bands, trial phases, failure archetypes, high-risk programs) for rapid diligence, distinguishing it from sibling tools like 'search_claims' which likely only returns raw claims.

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 'rapid diligence' but does not explicitly state when to use versus when not to, nor does it mention alternative tools or prerequisites.

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 behavioral traits. It states what is returned but does not mention whether the tool is read-only, idempotent, or has any side effects, leaving behavioral assumptions to the agent.

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 sentence that directly states what the tool returns. It is efficient with no 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 parameters and no output schema, the description adequately covers the tool's functionality. However, it lacks details on output format or interpretation, which would be helpful but not critical for a simple snapshot 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?

There are no parameters, and the schema coverage is 100% (empty). The description adds value by listing the returned metrics, which goes beyond the schema and helps the agent understand the output.

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

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

The description explicitly lists what the tool returns (Sentinel metrics, coverage, etc.), making the purpose clear. However, it does not distinguish it from sibling tools like claidex_capabilities or claidex_claim_risk_matrix, leaving potential ambiguity.

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

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

No guidance is provided on when to use this tool versus alternatives. The description only lists outputs, omitting context about appropriate use cases or prerequisites.

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 states the tool composes a prompt but does not disclose read-only vs mutating behavior, auth needs, rate limits, or other traits. Given the tool likely returns a string without side effects, the transparency is adequate 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?

The description is a single, direct sentence that immediately conveys the tool's purpose. It is concise, front-loaded, and contains no extraneous information.

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

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

Given the tool has 4 parameters, 2 required, and no output schema, the description is brief. It does not explain the output format or how the prompt should be used. However, the tool is simple and the name/description provide sufficient clues for an AI agent to understand its basic function. It is minimally 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 75% but the risk_tolerance parameter lacks a description. The tool description does not add any extra meaning beyond what the schema provides for the other parameters. It fails to compensate for the missing schema description, leaving semantics incomplete.

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 composes a reusable investigation prompt, using the verb 'compose' and specifying the resource 'prompt'. It distinguishes itself from sibling tools, which are actual data retrieval tools, by being a meta-tool for generating prompts.

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

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

The description implicitly indicates use when needing to create a reusable investigation prompt, but lacks explicit guidance on when to use versus alternatives. No usage context or exclusions are provided, making it minimally adequate.

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

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

With no annotations, the description carries full burden. It discloses the main behavior (returning catalog with optional filtering/schema inclusion) but does not mention safety aspects like read-only nature or potential size of output.

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 sentence that front-loades the key action and resource. Every word adds value.

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

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

The tool is simple with 2 parameters and no output schema. The description covers all essential information: what it does and the main options. It is complete for an agent to use correctly.

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

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

Schema coverage is 50% (only include_schemas described). The description reinforces the filtering and schema inclusion options, adding some meaning beyond the schema's enum for source. However, it does not fully compensate for the undocumented source parameter's purpose.

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 complete MCP tool catalog, with options for including schemas and filtering by source. It distinguishes itself from sibling tools, which are individual data or action 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?

No explicit guidance on when to use this tool over alternatives is provided. The description implies its use for discovering available tools, but lacks when-not-to-use or prerequisite information.

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 outlines the tool's function but does not disclose behavioral traits like latency, cost, state changes, or limitations beyond the schema.

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 efficiently convey purpose and use cases with no wasted words. Front-loaded with key 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?

Covers purpose and usage but does not describe return format or error behavior, which would be helpful for a model-calling tool given 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?

Schema description coverage is 100%, so the schema already explains parameters. The description lists some task types but adds minimal new meaning 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?

Description clearly states it consults a specialist model for code, database, statistics, etc. and lists use cases, but does not explicitly differentiate from sibling tools like query_chembl or search.

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

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

Provides explicit use cases ('hard subproblems', 'second opinions', etc.), giving clear context for when to use, but lacks exclusion criteria or alternative recommendations.

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, and the description lacks behavioral details such as error handling, side effects, or return behavior, relying on minimal context.

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

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

Two concise sentences with no wasted words, front-loading the primary purpose effectively.

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 simple parameters, the description is minimally adequate but lacks details on error scenarios and return values, leaving some 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% and the parameter description in the schema is already adequate; the tool description adds no new semantic value beyond restating the schema.

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

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

The description clearly states the tool fetches a specific result from search, distinguishing it from sibling tools like search and fetch_research_url.

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 after search to retrieve a specific result, providing clear context but no explicit when-not-to-use or 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 must carry behavioral transparency. It discloses two key behaviors: blocking private hosts and truncating large responses. Missing details like rate limits, authentication, error handling, or response format.

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, front-loaded with purpose and then behavioral constraints. No redundant 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 and only 2 parameters, description adequately covers purpose, usage constraints, and parameter context. Could mention error handling or response format 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?

Schema coverage is 50% (only 'url' described). Description adds context that URLs must be public HTTPS and that private hosts are blocked, which relates to 'url' parameter. Does not add meaning for 'max_chars' beyond schema defaults.

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 verb 'Fetch' and resource 'public HTTPS research URL', listing specific URL types (JSON API, package metadata, documentation, dataset). Differentiates from generic sibling 'fetch' by specifying 'research' scope.

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 mentions blocking private/internal hosts and truncation, which implies usage for public URLs with large responses. However, no explicit alternatives or when-not-to-use beyond internal hosts, and no comparison to siblings like 'web_search' or 'search'.

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

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

With no annotations, the description clearly indicates a read operation ('retrieve') and details the content returned. It does not mention error handling or side effects, but for a simple retrieval, the behavior is adequately transparent.

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

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

Two concise sentences with no filler. Every word adds value: verb, resource, usage context, and content summary.

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 covers purpose, usage, and return content. Minor omission: does not specify format (e.g., plain text or HTML), 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?

The schema already describes the slug parameter with an example. The description adds context by explaining what the retrieved content includes, which helps the agent understand the value of providing a slug.

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 ('retrieve'), the resource ('complete text content of a specific Claidex Claim'), and the method ('by its slug'). It also specifies the content includes 'hypothesis, failure mechanism, and prevention analysis', distinguishing it from sibling tools like 'search_claims'.

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

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

The description explicitly says 'Use this when you need to read the full post-mortem analysis', providing clear context. However, it does not mention when not to use this tool 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, and the description does not disclose behavioral traits such as read-only nature, rate limits, authentication needs, or error handling. It only lists return fields without any constraints or side effects.

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

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

Single sentence that is front-loaded with the purpose and includes specific output details. No redundant information; every part is necessary.

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

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

With no output schema, the description appropriately lists the return fields. However, it lacks information on error cases (e.g., unknown drug) and could mention whether partial matches are supported. Overall, it is sufficiently complete for a simple 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?

The schema has 100% coverage with one parameter 'drug_name' described as 'Drug or compound name'. The description adds value by explaining what will be returned (mechanism of action, etc.), which enriches the parameter's 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?

The description clearly states the verb 'look up' and the resource 'drug or compound in ChEMBL', listing specific return fields. This distinguishes it from sibling tools like query_clinicaltrials and query_openfda by naming the target database and outputs.

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 drug lookup in ChEMBL but provides no explicit guidance on when to use this tool versus alternatives like query_opentargets or query_clinicaltrials. No exclusion criteria or prerequisites are mentioned.

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

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

No annotations are provided, so the description carries full burden. It discloses return fields (trial status, phase, sponsor, primary endpoints, why_stopped if terminated) which gives insight into behavior. However, it does not mention side effects, rate limits, authentication, or pagination.

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, front-loaded with the main action, and every word adds value. No fluff or repetition.

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 4 parameters, no output schema, and no annotations, the description covers search scope and return fields but misses important context like enum meanings, result limit (max_results max 10), and pagination. Fairly complete for a simple search tool but could be more thorough.

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 only 25% (only 'query' has a description). The tool description partially compensates by explaining 'query' can search by drug, condition, etc., but it does not explain the meaning of phase or status enum values, nor the behavior of max_results. More parameter explanation is needed given low coverage.

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

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

The description clearly states the tool searches ClinicalTrials.gov for clinical trials, specifying searchable fields (drug name, condition, target, NCT ID) and return fields. This distinguishes it from sibling tools like query_chembl or query_openfda.

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?

Usage is implied by the description—use when needing clinical trial data—but there is no explicit guidance on when to choose this over siblings like query_chembl or query_opentargets. The description hints at use cases (search by drug, condition, etc.) but lacks 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 are provided, so the description must carry the burden. It describes outputs but does not explicitly state if the tool is read-only or any constraints. It implies a read operation but lacks a safety declaration.

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?

One sentence that front-loads the action and lists key outputs. No redundant information. Efficiently sized.

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

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

For a simple query tool with one parameter and no output schema, the description sufficiently covers purpose and outputs. However, lacking detail on return format or error handling. Still adequate.

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

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

The input schema fully covers the parameter with a description. The tool description reinforces usage (HGNC symbol) and provides examples. This adds value beyond the schema.

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

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

The description clearly states the tool queries the Claidex failure graph for a specific gene target and lists specific outputs (MRS, failure counts, claim slugs), distinguishing it from sibling tools like query_chembl or query_opentargets.

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 vs alternatives. The purpose implies use for failure analysis but lacks context on when not to use or mentions of other tools.

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

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

With no annotations, the description indicates a read-only query (via 'Query') and lists return fields. It does not mention rate limits or side effects, but for a simple data retrieval tool, the behavioral traits are adequately disclosed.

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

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

A single sentence front-loads the primary action and resource, then specifies return values. Every word is necessary; no filler or redundancy.

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

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

Without an output schema, the description lists return elements but omits structure, error handling, or default behavior. For a tool with no annotations and no output schema, it provides the essentials but lacks depth.

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 50% (drug_name described, max_results not). The description only references drug_name via 'for a drug', adding no additional meaning beyond the schema. The schema itself provides constraints for max_results, but the description does not compensate for the missing property 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 queries the FDA FAERS database for adverse event signals, specifying it returns top adverse event terms, serious case count, and death count. This verb-resource-output structure leaves no ambiguity and distinguishes it from siblings querying other databases.

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

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

The description implicitly indicates use for adverse event data from FAERS. While it does not explicitly contrast with siblings like query_chembl or query_clinicaltrials, the domain is clear. No when-not-to-use or alternative guidance is provided, but the tool's name and description are sufficiently unique.

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 only lists return types without disclosing behavioral traits such as read-only nature, rate limits, or authentication needs. As a query tool, safety is implied but not stated.

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 clear front-loading: action and resource in first sentence, output details in second. No redundant content; each word contributes to clarity.

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, description adequately explains return values (genetic score, clinical evidence, drugs, tractability, safety). Missing information on result limits or pagination, but sufficient for a 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?

Input schema covers both parameters with descriptions (HGNC symbol, disease name/EFO). Schema description coverage is 100%, and description adds no new semantic details beyond repeating parameter names in context.

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

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

Description clearly states 'Query Open Targets for target-disease association evidence' with a specific verb and resource, and lists return components. Differentiates from sibling tools like query_chembl and query_clinicaltrials.

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 specifies input as a gene-disease pair and enumerates returned evidence types, effectively implying when to use. Lacks explicit comparison with alternatives or exclusion conditions, but context on data sources 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It indicates a read-only operation ('look up'), but does not mention rate limits, authentication, or error handling. The description is adequate but lacks detailed 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 a single sentence that is front-loaded with the verb 'Look up'. It is concise and avoids redundancy, though it could include more detail without becoming verbose.

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 tool has no output schema, yet the description does not explain what metadata is returned (e.g., version, description, dependencies). Error handling and edge cases are not addressed. The description is somewhat incomplete for a tool that queries external registries.

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 each parameter having a basic description. The description does not add further meaning beyond the schema, such as case sensitivity, exact match requirements, or format 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's function: looking up package metadata from npm or PyPI. It also specifies the context of use ('before recommending code, analysis packages, API clients, or dataset tooling'), distinguishing it from sibling tools like search or web_search.

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

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

The description implies when to use the tool (before recommendations), but does not explicitly state when not to use it or provide alternative tools. However, the context is clear enough for an agent to infer appropriate usage.

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

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

No annotations are provided, so the description carries the full burden. It states the tool embeds and ranks using cosine similarity, but does not disclose any behavioral traits such as side effects, authentication needs, rate limits, or whether the operation is read-only. The description is adequate but not deep.

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

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

A single front-loaded sentence stating the core action, followed by a compact list of use cases. No wasted words. Every sentence serves a purpose.

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

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

For a tool with three required parameters, no output schema, and no annotations, the description covers the purpose, method, and typical use cases. It does not detail output format or error behavior, but overall it provides sufficient context for an agent to understand the tool's functionality.

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

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

Schema description coverage is 0%, so the description must compensate. It mentions 'query' and 'candidate documents' which map to the parameters, but does not provide additional detail about constraints like max lengths or the meaning of top_k beyond the default. The addition is marginal, earning a baseline score.

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 ('Embed a query and candidate documents, then rank documents by cosine similarity') and lists specific use cases (semantic matching, retrieval checks, clustering triage, lightweight RAG). This distinguishes it from sibling tools like rerank_documents which likely uses a different 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?

The description provides clear context for when to use the tool by listing use cases. However, it does not explicitly state when not to use it or provide alternative tool names. The context signals include siblings like rerank_documents, but the description itself does not differentiate them.

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

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

With no annotations, the description must fully disclose behavior. It mentions using a dedicated model for exact relevance ordering, implying a non-destructive reordering. However, it does not mention side effects, rate limits, or output format. 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?

Two sentences, front-loaded with purpose, followed by usage context. No redundant 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?

For a simple reranking tool with no output schema or annotations, the description covers purpose and usage adequately. However, lack of information about return format and error conditions is a minor gap.

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

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

Schema coverage is 0% (no parameter descriptions), so the description should compensate. It only mentions 'documents/passages against a query' and implicitly 'top_n' via the schema. No individual parameter explanation is given, leaving reliance on field names.

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 'rerank', the resource 'documents/passages against a query', and specifies using a dedicated reranking model. It distinguishes from siblings like 'rank_documents_by_embedding' and search tools.

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

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

The description explicitly says 'Use after retrieval/search when exact relevance ordering matters', providing clear when-to-use guidance. While it does not mention when not to use or list alternatives explicitly, the context from sibling tools implies appropriate usage.

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

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

No annotations are provided, so the description must convey behavioral traits. It states the tool is 'deterministic' and 'reproducible,' which is useful. However, it does not disclose side effects, error handling, or performance characteristics. With no annotations, a 3 is appropriate as it provides some context but leaves gaps.

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 defines the core purpose, the second provides use cases and an explicit exclusion. It is front-loaded, concise, and every sentence adds value. No 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 the tool's complexity (2 params, no output schema), the description is largely complete. It explains the tool's domain, limitations, and examples. However, it does not describe the return value format (e.g., a number or matrix), which would help the agent handle results. Nonetheless, it covers the essentials for selection and invocation.

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

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

Schema coverage is 100%, so the schema documents both parameters. The description adds value by giving example expressions (e.g., 'mean(values)', 'multiply(A, b)') which clarify usage beyond the schema's property descriptions. This helps the agent understand the parameter semantics better.

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 'Evaluate deterministic mathematical expressions with optional variables' and provides specific use cases (algebra, matrices, vectors, etc.). It also explicitly states what it does not do (execute arbitrary JavaScript or install packages). This gives a precise purpose and distinguishes it from sibling tools like run_statistical_analysis.

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

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

The description lists concrete use cases and exclusions, helping the agent decide when to use this tool. However, it does not explicitly contrast with sibling tools like run_statistical_analysis or query_* tools, which would have been helpful. The guidance is clear but not exhaustive.

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 describes outputs (computed values, interpretation, LaTeX) but does not disclose side effects, permissions, error handling, or assumptions. 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?

Two sentences: first states purpose, second lists supported types and outputs. Perfectly concise and front-loaded with no wasted words.

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

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

For a tool with multiple analysis types and no output schema, it describes outputs adequately. Could be improved with usage hints or error behavior, but overall complete enough.

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?

Input schema covers parameter details with 67% description coverage; the schema's 'inputs' description is quite detailed. The tool description only lists supported types but adds no parameter semantics beyond the schema. Baseline 3 is appropriate.

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

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

Clearly states the verb 'Run' and resource 'statistical analysis computation', lists supported analysis types. However, does not explicitly differentiate from sibling 'run_computation', though context makes it clear.

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?

Lists supported analyses but provides no guidance on when to use this tool versus alternatives, no prerequisites or when-not-to-use. The description lacks explicit 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?

No annotations are provided, so the description must fully disclose behavioral traits. It states 'returns citation-ready results', implying a read-only operation, but does not explicitly state it is non-destructive, mention permissions, rate limits, or any side effects. The disclosure is minimal.

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 long, efficiently stating the action and a contextual note. It is front-loaded with the core purpose. The second sentence, while adding context, could be integrated but does not waste 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 simplicity (2 parameters, no output schema, no annotations), the description covers the basic purpose and output format. However, it does not specify the exact format of citation-ready results or how results are paginated. It is adequate but has gaps, especially compared to sibling tools with more detailed names.

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 50%, with only 'query' having a description ('Biomedical failure intelligence query.'). The description adds no further parameter semantics for 'limit' or beyond. The coverage is borderline, and the description does not compensate for the missing parameter descriptions.

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

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

The description clearly states the verb 'Search' and the resource 'Claidex failure claims and preprint flags', indicating a specific database. It distinguishes somewhat from sibling tools like search_claims and search_preprint_flags by combining both. However, the term 'Claidex' is not defined, and the compatibility context adds ambiguity.

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 'compatibility tool for OpenAI and ChatGPT remote MCP flows', which hints at a specific use case but does not provide explicit when-to-use or when-not-to-use guidance. It offers no comparison to the many sibling tools (e.g., search_claims, search_pubmed).

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 describes return fields but does not disclose behaviors such as authentication requirements, rate limits, or what happens on empty results, beyond what is implied by a search tool.

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

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

The description is a single, well-structured sentence that front-loads the core purpose and lists return fields without extraneous 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 and no annotations, the description covers the essential search behavior and return fields. It lacks some contextual details like pagination or result count, but is 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 description coverage is only 25% (only the query parameter has a description). The description adds meaning by listing context (drug, target, disease) but does not fully explain each parameter. The enum parameters have no descriptions in schema, and the description only broadly mentions them.

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 a failure database for post-mortems matching specific criteria (drug name, gene target, disease, failure archetype, or sponsor) and returns structured fields. This is specific and distinguishes it from sibling tools like search_pubmed or query_clinicaltrials.

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 finding post-mortems by mentioning searchable criteria and returned fields, but it does not explicitly state when to use this tool versus alternatives like get_claim_content or query_failure_graph.

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 convey behavioral traits. It discloses the source (bioRxiv) and condition (contradicting clinical failure), but does not explain what 'flagged' means, result limits, or potential edge cases. 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?

Two concise sentences, front-loaded with the tool's purpose. No unnecessary words. Efficiently communicates key information.

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

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

Given the simplicity (2 params, no output schema), the description covers the essential context: what to search, how to search. Missing details on output format or result interpretation, but overall adequate for a straightforward 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 50%: 'query' has a description aligned with the tool's purpose, and 'severity' has an enum but no description. The tool description adds no additional parameter meaning beyond the schema. Baseline score 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 searches bioRxiv preprints flagged as contradicting clinical failure records, with specific search dimensions (target gene, disease, preprint title). This distinguishes it from sibling search tools like search_claims or search_pubmed.

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 context by specifying what to search for, but lacks explicit guidance on when to use this tool vs alternatives or when not to use it. No comparisons with sibling tools are provided.

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

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

No annotations exist, so the description must disclose behavioral traits. It only lists return fields but omits mention of rate limits, authentication, or non-destructiveness. For a read operation, stating it is read-only would be beneficial.

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, front-loaded sentence that efficiently conveys the tool's purpose and output. No 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 the simplicity (2 parameters, no output schema), the description adequately lists return fields. However, it lacks details on pagination, date range, or sorting, which could enhance completeness 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 description coverage is 50% (only 'query' has a description; 'max_results' lacks one). The description adds no parameter details beyond the schema, failing to compensate for the missing schema description of 'max_results'.

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 PubMed for biomedical literature and lists specific return fields (title, authors, journal, year, DOI, abstract, PMID). It distinguishes from sibling tools like 'fetch' and 'web_search' by specifying the database and content type.

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 versus alternatives is provided. While the description implies use for biomedical literature, it does not mention exclusions or compare with sibling tools like 'query_clinicaltrials' or 'search'.

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

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

No annotations are provided, and the description lacks any behavioral details such as result count, freshness, rate limits, or output structure. The description only covers what content is searched, not how the tool behaves.

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 sentence that is front-loaded with the core purpose and covers key content types without unnecessary words. It is efficient and 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?

The tool has no output schema, so the description should ideally describe return format or result structure. It also lacks details on limitations, pagination, or result count. For a simple search tool, some additional context about results is expected.

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

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

The description lists content types that partially map to the focus enum (news, academic, investor, any) but does not explicitly explain the parameter's role. Schema description coverage is 50% (only query has description), so the description adds some value but does not fully clarify parameters.

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

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

The description clearly states the tool searches the web for specific biomedical content like news, investor commentary, analyst notes, press releases, and conference coverage. It distinguishes from siblings like search_pubmed (academic) and generic search.

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

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

The description implies use for recent biomedical content but does not explicitly state when to use this tool versus alternatives (e.g., search_pubmed for academic papers) or provide exclusions.

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