Claidex MCP

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

No annotations are provided, so the description carries full burden. The verb 'Return' implies a read-only operation, but the description does not explicitly state that it has no side effects, nor does it disclose any configuration dependencies or potential error conditions. It adds context about the result content but nothing about operational behavior.

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

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

The description is a single sentence that front-loads the verb 'Return' and lists the specific content categories concisely. There is no wasted wording or irrelevant detail.

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 parameterless tool, the description adequately names all major output categories (feature map, providers, safety controls, resources, prompts, tool counts). However, it does not define the structure of the feature map or what a 'tool count' entails, and there is no output schema to compensate; still, it gives a clear enough picture for the agent to know what it will get.

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?

This tool has zero parameters, so the input schema is complete by definition. The description does not need to elaborate on parameter meanings, and it doesn't; this meets the baseline for a parameterless tool.

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

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

The description uses the specific verb 'Return' and identifies the exact resource: the Claidex MCP feature map including providers, safety controls, resources, prompts, and tool counts. This clearly distinguishes it from sibling tools like claidex_tool_catalog, which likely list individual tools rather than overall 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?

No guidance is provided for when to use this tool versus alternative Claidex tools. For example, there is no mention of using this to discover capabilities before invoking other tools, nor any distinction from claidex_tool_catalog. This leaves the agent to infer usage from the name alone.

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 correctly describes the tool as searching and summarizing, indicating a read-oriented operation. However, it does not disclose details such as whether results are full claims or only summaries, any dependencies, or potential side effects beyond the basic operation.

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

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

The description is a single sentence that front-loads the verb and includes only essential information. It avoids filler and is highly scannable, making it easy for an agent to parse.

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 should clarify return content and context. It lists what is summarized, but omits details on output format, how the limit parameter behaves, and how the tool relates to similar claim-search tools. It is adequate but leaves some gaps for a 2-parameter 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% (query has a description, limit does not). The tool description does not mention parameters or compensate for the missing limit description. While 'limit' is somewhat self-explanatory, the description adds no value in clarifying parameter usage or constraints.

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

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

The description clearly states a specific verb ('Search' and 'summarize') and a defined resource ('claims') with a distinct outcome ('MRS bands, trial phases, failure archetypes, high-risk programs'). It differentiates from sibling tools like search_claims by adding the summarization role, making its 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 phrase 'for rapid diligence' implies a use case, but there is no explicit guidance on when to use this tool versus alternatives like search_claims or query_failure_graph. No exclusions or comparison to siblings are provided, leaving the agent infer 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?

With no annotations provided, the description carries the full responsibility for disclosing behavior. It states 'Return' which implies a read-only operation, but it does not mention whether the snapshot is real-time, cached, or if it has any side effects or limitations. This is a minimal disclosure.

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 lists all returned items without unnecessary words. It is well-structured and easy to parse.

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

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

Given that the tool has no parameters and no output schema, the description lists the expected return content, which provides a baseline understanding. However, it lacks explanations of key terms like 'MRS bands' and 'Sentinel metrics', and does not describe the output format, leaving the description minimally sufficient but incomplete for a tool with no 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 tool has zero parameters and an empty schema, so the description does not need to explain parameter semantics. It correctly avoids adding redundant parameter information.

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

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

The description uses the specific verb 'Return' and enumerates six distinct data categories (Sentinel metrics, coverage, graph counts, recent automation runs, MRS bands, preprint-watch summaries), making the tool's function clear. However, it doesn't explicitly differentiate itself from sibling tools that may return subsets of this data, so it stops short of full differentiation.

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 such as query_failure_graph or search_preprint_flags. The description only lists the return contents, leaving the agent to infer use cases.

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 burden of behavioral disclosure. It explains the primary action (composing a prompt) but does not state whether the tool executes tools, returns a string, or has side effects. The word 'compose' implies generation, but more detail would improve 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?

Description is a single, well-structured sentence with no redundancy. Each word contributes meaning, and the key action and object are 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?

The tool has 4 parameters, no output schema, and no annotations. The description provides the core purpose but lacks information about the return value, how inputs shape the prompt, or what makes a prompt 'rigorous'. Given the moderate complexity, the description is minimally sufficient 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?

The description adds no parameter-level meaning. Schema coverage is 75% (disease, objective, target_gene have descriptions), but risk_tolerance is undocumented in both schema and description. The description does not explain how objective or risk_tolerance influence the generated prompt, missing an opportunity to compensate for the coverage gap.

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

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

The description uses the specific verb 'compose' and identifies the resource as a 'rigorous, reusable investigation prompt'. It clearly distinguishes this tool from sibling research query tools by indicating it produces an MCP client prompt rather than executing research itself.

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 stating the prompt 'tells an MCP client which Claidex tools and resources to use,' making it clear this is for prompt generation rather than direct research. However, it does not explicitly mention alternatives or when not to use it, so it falls short of full explicit 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?

No annotations are provided, so the description must carry the full behavioral disclosure burden. It states the tool returns a complete catalog and supports filtering, but it does not disclose the return format, whether the catalog includes descriptions, the potential size of the output, or what 'complete' means. It also leaves the terms 'native' and 'agent-backed' undefined, which could confuse 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 with the primary action ('Return the complete MCP tool catalog') front-loaded. It adds relevant optional details in a compact manner without any redundant words. Every phrase contributes to the understanding of the tool's functionality.

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

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

Given that there is no output schema, the description should clarify the structure of the returned catalog (e.g., list of tool names, descriptions, metadata). It does not. Also, the schema marks both parameters as required despite having defaults, and the description does not explain behavior when defaults are used. This leaves important operational gaps for the agent.

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

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

The schema description coverage is only 50% (include_schemas has a description, source does not). However, the tool description compensates by explicitly referencing 'full input schemas' for include_schemas and 'filtering native versus agent-backed tools' for source, effectively clarifying the purpose of both parameters. This goes beyond the schema and provides meaningful semantic mapping.

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 action: 'Return the complete MCP tool catalog.' It also specifies two optional capabilities (including full input schemas and filtering by source), which precisely distinguishes this tool from the sibling tools that are domain-specific search or query tools. The verb 'return' plus the resource 'MCP tool catalog' makes 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 does not provide any when-to-use guidance or comparison with alternative tools. It does not mention exclusions, prerequisites, or situations where this tool should be preferred over siblings. The catalog nature implies a discovery use case, but the agent is left to infer this from the tool name and sibling context rather than explicit 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?

No annotations are provided, so the description carries the full burden. While it says 'Ask a specialist Gateway model', it does not disclose behavioral traits such as latency, output format, token limits, cost, non-determinism, or side effects. The capability list (code, statistics, etc.) describes what it can be asked for, not how it 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 two sentences with a front-loaded verb ('Ask') and immediately conveys purpose and usage. Every word earns its place; 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 no output schema and no annotations, the description lacks details about return value, limitations, or cost/performance considerations. It adequately introduces purpose and use cases, but leaves open questions about what the user will receive and any trade-offs.

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 includes descriptions for all parameters, so baseline is 3. The description adds a list of supported domains that partially mirrors the task_type enum, but does not deepen 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 that the tool asks a specialist Gateway model for support across code, database/tool-calling, statistics, literature, preprint, and synthesis domains. It distinguishes from sibling tools by positioning it as a consultation for hard subproblems, second opinions, and complex synthesis, not a direct search or computation 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 explicitly lists usage scenarios: hard subproblems, second opinions, code generation/review, and complex synthesis. It provides clear context for when to use, but does not explicitly name alternatives or when not to use it, so it stops short of a perfect 5.

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

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

No annotations are present, so the description must carry safety and behavior disclosure. It states that this is a compatibility tool for remote MCP retrieval flows, but does not explicitly confirm read-only behavior, error handling, or return format. The description adds minimal behavioral context beyond the tool's name and title.

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

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

Two sentences, front-loaded with the core action, no redundant filler. Every word 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 one-parameter tool with no output schema, the description provides adequate context: it identifies the predecessor (search) and the target use case. It could mention return behavior but isn't critically missing for this 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 input schema covers 100% of the parameter with description, and the tool description adds no additional parameter semantics. Baseline 3 applies.

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

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

The description uses a specific verb ('Fetch') and identifies a resource ('specific result returned by search'), distinguishing it from search tools and the sibling fetch_research_url. It also explains why this tool exists, as a compatibility shim for OpenAI/ChatGPT MCP flows.

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

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

It explicitly frames this as a compatibility tool for OpenAI/ChatGPT remote MCP retrieval flows, giving clear when-to-use context. It doesn't name alternatives or exclusions, but the usage context is reasonably 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 provided, the description carries the full burden of behavioral disclosure. It explicitly mentions blocking private/internal hosts (SSRF protection) and truncating large responses, which are key behaviors. However, it does not mention error handling, redirects, 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?

The description is two sentences, front-loaded with the purpose, and includes only relevant behavioral notes. Every word 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?

With only two parameters and no annotations or output schema, the description covers the core use case and key behaviors. However, it lacks details on error responses, redirects, or content handling, which would make it fully 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 documents url but not max_chars. The description only vaguely refers to 'truncates large responses,' which does not fully explain the max_chars parameter's purpose or truncation behavior. It adds minimal 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?

The description clearly states the tool fetches public HTTPS research URLs, listing specific resource types (JSON API, package metadata, documentation, dataset preview). This verb+resource+scope structure distinguishes it from sibling tools like web_search or query_*.

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 gives no explicit guidance on when to use this tool over alternatives like fetch or web_search, nor does it specify exclusions or prerequisites beyond blocking private hosts. The use case is implied but not directly 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?

No annotations are provided, so the description must carry the behavioral burden. It discloses that the tool retrieves text content and lists included sections (hypothesis, failure mechanism, prevention analysis), but does not mention error behavior, authorization, or response format. This is acceptable for a read operation 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?

Two sentences, front-loaded with the primary action, followed by a clear usage context. Every word earns its place; no redundancy or fluff.

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

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

For a single-parameter retrieval tool without an output schema, the description adequately explains what is returned (full text content and its components). It lacks details like response formatting or edge cases, but these are not critical for a simple read operation.

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 descriptive slug parameter including an example. The description merely repeats 'by its slug' without adding format or usage details, so it adds little 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 action ('Retrieve') and the specific resource ('complete text content of a specific Claidex Claim by its slug'). It distinguishes from siblings like search_claims (searching) and claidex_claim_risk_matrix (risk matrix) by focusing on reading full content.

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

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

Provides explicit context for when to use: 'Use this when you need to read the full post-mortem analysis'. It does not mention when not to use or explicitly name alternatives, but the 'when' guidance is sufficient for a distinct read 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?

With no annotations, the description takes on the burden of behavior disclosure. It states the operation is a lookup and lists return fields, implying a read-only, non-destructive action. However, it does not disclose behavior on invalid input, empty results, or error handling, leaving some uncertainty for 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, front-loaded sentence with no fluff or repetition. It efficiently conveys the verb, resource, and expected outputs, achieving high clarity in minimal 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 (one parameter, no output schema), the description is complete: it states what the tool does, what input is needed, and what fields are returned. The listed return values compensate for the absent output schema adequately 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?

Schema coverage is 100%, as the only parameter drug_name is described as 'Drug or compound name'. The description adds minimal semantic value beyond the schema, reinforcing that a name is expected, but providing no additional format, examples, or constraints.

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

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

The description clearly states the tool looks up a drug or compound in ChEMBL, which is a specific resource and distinguishes it from sibling tools like query_clinicaltrials or query_openfda. It also enumerates the return fields, making the tool's 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 clear context for when to use the tool (when needing ChEMBL drug/compound data), but it does not explicitly mention exclusions or alternatives. The specificity to ChEMBL implicitly guides the agent away from siblings, but explicit when-not-to-use guidance is 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?

No annotations are provided, so the description is the sole source of behavioral transparency. It discloses the return fields (status, phase, sponsor, primary endpoints, why_stopped if terminated), adding useful context beyond a bare 'search' description. However, it omits potential limitations such as pagination, result count behavior, or error scenarios.

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, action-first, and every clause adds value. It efficiently covers purpose, search options, and return fields without repetition or filler.

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

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

Without an output schema, the description carries the burden of explaining return values; it lists key fields and notes the special 'why_stopped' condition. It also mentions search flexibility. It lacks details on result limits or sorting, but for a moderate-complexity search tool it is mostly 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 only 25% (only 'query' has a description), so the description must compensate. It mostly repeats the schema's query description and provides no additional explanation for status, phase, or max_results. Although the schema's enums and defaults give some meaning, the description fails to bridge the coverage gap.

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 'Search ClinicalTrials.gov for clinical trials' with specific search modalities (drug name, condition, target, NCT ID) and lists return fields. This distinguishes it from sibling query tools like query_chembl and query_openfda by making the clinical trial domain explicit.

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 makes the use case evident (searching clinical trials) but does not explicitly say when to choose this over alternatives like web_search or query_pubmed. It provides no exclusions or comparisons to sibling tools, leaving usage guidance implicit.

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 burden. It discloses what the tool returns (MRS, failure counts, claim slugs), but it does not explicitly state that it is read-only or mention any side effects, rate limits, or error handling. The term 'query' hints at read-only behavior but is not explicitly 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?

The description is concise: two sentences, first states the action and scope, second lists the outputs. There is no redundant information or filler. It is well-structured and front-loaded with the primary 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 simple one-parameter query tool, the description provides the essential information: the resource, the parameter, and the return values. No output schema exists, so the description correctly explains the outputs. Minor omissions like prerequisites or limitations (e.g., whether the gene must already exist in the graph) prevent a perfect score.

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 100% of the parameter with a clear description and examples (HGNC gene symbol). The description adds no new parameter semantics beyond the phrase 'specific gene target,' which merely parallels the parameter name. The baseline of 3 applies since the schema provides full 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 action ('Query') and the specific resource ('the Claidex failure graph') for a specific gene target. It also lists the return values (Mechanism Risk Score, failure counts, claim slugs), distinguishing it from sibling query tools like query_chembl 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 when a gene target is known and failure graph data is needed, but it does not explicitly state when to use this tool versus alternatives or provide exclusions. No when-not-to-use guidance is given.

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

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

No annotations are provided, so the description carries the full burden. It discloses the main behavioral outcome—returning top adverse event terms, serious case count, and death count—which is informative for a query tool. However, it omits details like matching behavior (exact vs. partial) or error handling, so it is not fully 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, focused sentence (24 words) with no filler. It front-loads the purpose and immediately lists expected outputs, making it efficient and easy to scan.

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

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

For a simple two-parameter query tool, the description covers the core purpose and outputs, which partially compensates for the lack of an output schema. However, it does not explain input format expectations (e.g., exact drug name matching) or the meaning of max_results, so the description is adequate but not fully 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 50% with only drug_name described; max_results lacks a textual description. The tool description mentions returning 'top adverse event terms' but does not explicitly state how max_results controls the number of results. This leaves max_results semantics partly undefined, though the description adds some 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 tool's function: querying the FDA FAERS database for adverse event signals. It uses a specific verb ('Query') and resource, and lists concrete outputs (top adverse event terms, serious case count, death count). This distinguishes it from sibling tools like query_chembl or query_clinicaltrials, which target different 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 implies use for FDA adverse event data via the mention of FAERS, but does not explicitly state when to prefer this tool over alternatives or provide exclusion criteria. No 'use when' or alternative tool names are given, leaving the agent to infer timing from the sibling list.

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 discloses the read-only nature implicitly via 'Query' and lists what it returns, but it does not mention potential limitations, rate limits, or result structure. This is 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 the tool's core function, and no wasted words. Every piece of detail (return types) is valuable for agent invocation.

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

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

For a read-only query tool with a moderate scope, the description covers the key outputs and the input pair. It lacks explicit return format or error behavior, but the listed evidence categories give the agent sufficient context to invoke and interpret results.

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 provides 100% parameter coverage with clear descriptions (HGNC gene symbol, disease name or EFO term). The description adds no additional parameter-level detail beyond restating the pair 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 specific verb 'Query', the resource 'Open Targets', and the focused scope of 'target-disease association evidence'. It enumerates the types of evidence returned, which differentiates it from sibling tools like query_chembl 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 provides clear context for when to use the tool: querying target-disease association evidence for a gene-disease pair. It does not explicitly name alternatives or exclusion criteria, but the specificity of the use case makes it reasonably clear when this tool is appropriate.

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 behavior. It implies a read-only lookup of metadata, but does not explicitly state lack of side effects, return format, or rate limits, making it minimally 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 a single sentence that efficiently states the action, target, and use case with no filler. It is appropriately sized for the tool's simplicity.

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 two-parameter lookup tool with no output schema, the description conveys the core purpose and usage context sufficiently. It could detail the returned metadata fields, but it is complete enough for an agent to select and invoke the tool.

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

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

The input schema already describes both parameters with 100% coverage ('Package registry to query' and 'Package name'), so the description adds no additional parameter-level 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?

The description uses the specific verb 'look up' and names the resource 'package metadata from npm or PyPI', clearly distinguishing it from sibling tools focused on searches, biomedical queries, or document fetching.

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 phrase 'before recommending code, analysis packages, API clients, or dataset tooling' provides clear contextual guidance for when this tool should be invoked. It does not explicitly name alternative tools, but the timing and purpose are well defined.

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 full burden. It discloses the algorithmic behavior (embedding and cosine similarity) and intended usage, but omits any description of the return value or error behavior. Since the tool ranks documents, users might need to know whether the output includes scores or just the order.

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

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

The description is two sentences: first explains the mechanism, second lists use cases. It is front-loaded and every word adds value with 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?

The tool has no output schema and no annotations, so the description should clarify what the tool returns. While 'rank documents' implies an ordered list, the description does not specify whether it returns the documents, indices, or similarity scores. Combined with the missing top_k semantics, the description is adequate but incomplete for a fully self-contained specification.

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 mentions 'a query and candidate documents' which maps to the query and documents parameters, but it does not explain what top_k does. With no schema descriptions, this leaves one of the three required parameters semantically unexplained. The description partially compensates but is 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 embeds a query and candidate documents, then ranks by cosine similarity, using a specific verb and resource. It also lists concrete use cases (semantic matching, retrieval checks, clustering triage, lightweight RAG), which distinguishes it from sibling tools like rerank_documents, which likely uses a different 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?

The description explicitly says 'Use for' and lists four use cases, providing clear context for when the tool is appropriate. It does not name alternatives or specify when not to use this tool, but the use-case framing gives sufficient guidance for 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions using a 'dedicated reranking model', which adds some context, but it does not state whether the operation has side effects, requires special authentication, or has rate limits. As a stateless scoring operation, the risk is low, but the description could be more 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?

The description is two sentences, each earning its place. The first states the core functionality, and the second provides usage guidance. There is no redundancy or filler.

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

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

The tool has no output schema, so the description should explain what the tool returns. It only says 'rerank candidate documents', which implies the output is reranked documents, but it does not specify the format (e.g., scores, ordering). It also lacks any mention of limitations or edge cases beyond what is in the schema. Given the tool's simplicity, this is a moderate 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?

The schema has 0% description coverage, but the description implicitly clarifies 'documents' as 'candidate documents/passages' and 'query' as the query against which they are reranked. However, 'top_n' is not mentioned in the description, and the semantics of the output ranking order are not explained. Overall, it adds some value but does not fully compensate for the lack of parameter details.

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

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

The description uses a specific verb 'rerank' and clearly identifies the resource (candidate documents/passages) and the context (against a query). It also differentiates from the sibling tool 'rank_documents_by_embedding' by mentioning a 'dedicated reranking model'.

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

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

The description explicitly states when to use the tool ('Use after retrieval/search when exact relevance ordering matters'), providing clear context. It does not explicitly name alternatives or state when not to use it, but the guidance is unambiguous enough for a 4.

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 burden. It discloses deterministic behavior and safety constraints (no arbitrary JS, no package installation). It does not mention return shape or error handling, but for a pure math evaluator this is substantial.

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 the primary action first and usage/exclusion logic second. 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?

The tool has only two well-documented parameters and no output schema. Description covers scope and exclusions, but lacks an explicit statement about return values or error behavior; for a simple evaluator this is mostly 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, but the description says 'with optional variables' while the schema requires the `variables` property. This is misleading; an agent may omit a required field. The description adds no helpful parameter semantics beyond schema.

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

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

States 'Evaluate deterministic mathematical expressions with optional variables,' giving a specific verb and resource. It distinguishes from siblings like run_statistical_analysis by focusing on algebra/matrices/vectors and explicitly non-JavaScript.

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

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

Explicitly lists intended use cases ('algebra, matrices, vectors, transformations, descriptive numerical checks, reproducible calculations') and exclusions ('Does not execute arbitrary JavaScript or install packages'). This provides clear when-to-use and when-not-to-use 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 carries the full burden. It discloses that the tool returns computed values, an interpretation, and a LaTeX formula, which gives useful output expectations. However, it does not explicitly state whether the computation has side effects, requires permissions, or handles invalid inputs, leaving some behavioral ambiguity.

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 primary action, and efficiently covers scope and return values without fluff. Every phrase 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?

The tool is moderately complex with six analysis types, nested input objects, and no output schema. The description covers scope and return content but lacks examples, error behavior, or output structure guidance. It is adequate but not complete for the tool's complexity.

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

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

Schema description coverage is 67%, and the input schema itself provides detailed per-analysis parameter examples (e.g., wilson_ci: {successes, n, confidence}). The description adds value by listing supported analysis types but adds no parameter-level detail beyond the schema, so the contribution is moderate.

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

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

The description clearly states the tool's purpose: 'Run a statistical analysis computation' and enumerates specific supported analyses (Wilson CI, binomial proportions, phase transition rates, power, Bayesian, descriptive stats, two-proportion comparisons). This distinguishes it from the generic sibling 'run_computation' and other query/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 implies when to use the tool—whenever a supported statistical analysis is needed—but provides no explicit guidance on when not to use it or how it compares to alternatives like 'run_computation'. The supported-types list gives some context, but exclusions and decision rules 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?

With no annotations, the description carries the full burden for behavioral disclosure. It mentions returning 'citation-ready results' but does not explain output format, pagination, limitations, or read-only status.

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 and front-loaded with the core purpose. The second sentence adds context, though 'compatibility tool' is vague and slightly detracts from 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?

There is no output schema, so the description should at least clarify the shape and nature of results beyond 'citation-ready'. Given the large sibling tool set, the description lacks sufficient context for an agent to reliably choose and invoke this 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 provides a description for 'query' and constraints for 'limit', but the description adds no parameter-level meaning. With only 50% schema coverage, the description does not compensate for the lack of detail on 'limit' or query formatting.

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 searches Claidex failure claims and preprint flags, with a specific verb and resource. However, the phrase 'compatibility tool' is ambiguous and sibling tools like search_claims and search_preprint_flags overlap, so differentiation is weak.

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 is given on when to use this tool versus alternatives such as search_claims or search_preprint_flags. The reference to OpenAI and ChatGPT remote MCP flows hints at a niche use case but does not state exclusions or selection 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?

With no annotations provided, the description carries the full burden. It discloses the search scope and return fields, but does not mention filtering behavior (category, failure_archetype), limits, pagination, or any potential quirks. The description also omits that 'sponsor' is a valid search term while the schema query description does not include it, creating a minor inconsistency.

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 core purpose and followed by a list of return fields. It avoids fluff and is appropriately sized, though it could be slightly improved by explicitly stating the filter parameters.

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 4 parameters, no output schema, and no annotations. The description explains the search scope and return fields but does not cover parameter semantics for filters, limit behavior, or edge cases. Given the moderate complexity and the lack of structured annotations, the description is incomplete for safe 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 only 25% (query has a description). The tool description adds meaning for the 'query' parameter by listing acceptable search terms (drug name, gene target, disease, failure archetype, sponsor), but it does not explain the 'category' or 'failure_archetype' filter parameters, nor how they interact with 'query' and 'limit'. This leaves the agent with an incomplete understanding of how to construct a 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 a specific verb ('Search') and a specific resource ('Claidex failure database'), and defines the scope of the search ('post-mortems matching a drug name, gene target, disease, failure archetype, or sponsor'). This distinguishes it from sibling tools like search_pubmed 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 usage for finding failure post-mortems in the Claidex database, but it does not explicitly state when to use this tool versus alternatives like query_failure_graph or get_claim_content. There is no explicit when-not-to-use guidance, leaving the agent to infer based on the database name.

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

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

The description conveys a read-only search behavior and the key filtering criterion, but with no annotations it doesn't disclose result format, pagination, or constraints. It is not misleading, but it 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?

Single sentence, direct, and front-loaded with the database and action. No waste or redundancy.

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

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

Given the tool's simplicity, the description covers the main purpose but misses severity semantics and result expectations. It is adequate but not fully complete for an agent to invoke it effectively.

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

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

The 'query' parameter is reinforced by the description, but 'severity' is not mentioned. The schema has no description for 'severity' and the tool description doesn't explain it, leaving its meaning ambiguous at 50% schema 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 it searches a specific database (Claidex Preprint Watch) for bioRxiv preprints flagged as contradicting the clinical failure record, and lists searchable fields (gene, disease, title). This distinguishes it from generic search tools like search_pubmed 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 it (when looking for preprints that contradict clinical failure records), but it doesn't explicitly state alternatives or exclusions. Given many sibling search tools, more explicit guidance would improve clarity.

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 transparently states that it returns a list with specific fields (title, authors, journal, etc.), which communicates the output structure. However, it does not disclose potential limitations like result ordering, pagination, or error behavior, which are relevant for 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 exceptionally concise, using two clearly separated sentences to state purpose and return fields. All content is essential, with zero redundancy or filler.

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

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

For a simple search tool with no output schema and no annotations, the description provides the essential context: what it searches and what it returns. Its brevity is acceptable given the tool's simplicity, but it lacks any guidance on when to favor it over closely related sibling tools, which would improve completeness in the broader tool 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?

The description adds no parameter-specific meaning beyond the schema. It does not mention how to formulate a query or that max_results controls the result limit. With 50% schema description coverage (query is described, max_results is not), the description fails to compensate for the missing parameter semantics, leaving the agent to infer the purpose of max_results from its schema attributes 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 uses the specific verb 'Search' with the resource 'PubMed' and clearly states the domain 'biomedical literature.' It enumerates the exact return fields (title, authors, journal, year, DOI, abstract, PMID), which unambiguously distinguishes it from sibling tools like query_chembl 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 implicitly suggests when to use the tool—when you need PubMed literature search results—by naming PubMed and biomedical literature. However, it does not explicitly contrast with alternatives or state exclusions, such as 'for clinical trials use query_clinicaltrials.' The guidance is implied through purpose rather than clearly articulated.

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 must disclose behavioral traits. It mentions 'recent' as a temporal filter but does not state that the tool is read-only, describe the return format (e.g., list of titles/snippets/URLs), or mention any rate limits or prerequisites. This minimal behavioral disclosure leaves the agent to infer most behavior from the tool name.

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

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

The description is a single, front-loaded sentence that efficiently lists the tool's scope without redundancy. Every phrase earns its place, and it avoids filler or restating the tool name.

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 (two parameters, no output schema, no nested objects), the description covers the core purpose and query scope. However, it lacks detail on expected return values or result structure, and without annotations, the agent is left without a complete picture of the tool's output. It is adequate but not fully comprehensive.

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

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

Schema coverage is only 50% (query has a description, focus does not). The description compensates by clarifying the purpose of 'focus' implicitly—listing 'news, investor commentary, analyst notes, press releases' aligns with the enum values 'news' and 'investor', and the biomedical domain clarifies the 'query' content. While not explicit field-by-field, it adds meaningful semantic context beyond the bare 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 identifies the tool's action ('Search the web') and its resource (web content), while specifying content types (news, investor commentary, analyst notes, press releases, conference coverage) and domain (drugs, clinical trials, biomedical mechanisms). This distinguishes it from sibling tools like search_pubmed (bibliographic database) and fetch (specific URL retrieval).

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 this tool: when seeking recent web content about drugs, trials, or biomedical topics. It implies a recency requirement and enumerates content types, allowing the agent to match user intent. However, it does not explicitly name alternatives or exclusion cases, so it falls short of a 5.

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