cityuikes

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 effectively describes key traits: it's a query tool that uses natural language, automatically selects data sources, and returns results. However, it lacks details on limitations (e.g., rate limits, error handling, or data freshness), which prevents a perfect score.

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

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

The description is front-loaded with the core purpose, followed by supporting details and examples. Every sentence adds value: the first explains the tool's function, the second clarifies its automation, and the third provides concrete use cases. It's efficient with zero wasted text.

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 (natural language querying with automated tool selection) and lack of annotations/output schema, the description does well by explaining the process and providing examples. However, it doesn't cover response formats or potential failures, leaving some gaps in completeness for an agent invoking it.

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

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

The schema description coverage is 100%, with the parameter 'question' fully documented in the schema. The description adds minimal value beyond the schema by emphasizing 'plain English' and providing examples, but doesn't elaborate on parameter constraints or formats. This meets the baseline for high 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 the tool's purpose: 'Ask a question in plain English and get an answer from the best available data source.' It specifies the verb ('ask'), resource ('answer'), and mechanism ('Pipeworx picks the right tool'), distinguishing it from sibling tools like discover_tools or search_networks by emphasizing natural language interaction without manual tool selection.

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 this tool: 'No need to browse tools or learn schemas — just describe what you need.' It provides clear alternatives (implicitly suggesting not to use sibling tools for direct queries) and includes examples like 'What is the US trade deficit with China?' to illustrate appropriate 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?

No annotations provided, so the description carries full burden. It discloses the return format (paired data + URIs) and the fields for each type. It does not mention error handling, rate limits, or authentication, but these are expected for a comparison tool. The description is transparent about what it does without contradictions.

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

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

Four sentences, each adding unique value. The first sentence states the core function. The next two detail per-type output. The last highlights efficiency. No fluff, front-loaded with purpose. Perfectly concise for the complexity.

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

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

Given no output schema and no annotations, the description covers the main aspects: input parameters, per-type behavior, and output format (paired data + URIs). It lacks specifics on response structure or error handling, but is complete enough for an agent to use correctly. Could mention potential errors or response size limits.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds value by explaining what each type returns (company vs drug) and providing example values for the 'values' parameter. This goes beyond the schema, helping the agent understand how to populate the array correctly.

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 compares 2-5 entities side by side, specifies fields for company (revenue, net income, cash, long-term debt) and drug (adverse-event reports, FDA approvals, active trials), and mentions returning paired data and pipeworx:// URIs. It distinguishes itself from sequential calls, fulfilling the 'specific verb+resource' and 'distinguishes from siblings' criteria.

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 says to use when comparing 2-5 entities and highlights efficiency (replaces 8-15 sequential calls). It does not explicitly state when not to use or provide alternatives, but the context is clear. Sibling tools like 'resolve_entity' are available but not mentioned. The guidance is adequate 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the search functionality and return format ('most relevant tools with names and descriptions'), but lacks details on error handling, authentication requirements, rate limits, or pagination. It adequately describes core behavior but misses advanced operational context.

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

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

The description is highly concise and front-loaded, with two sentences that directly communicate purpose and usage guidelines without redundancy. Every phrase adds value, such as specifying when to call it first and the return format, making it 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?

Given the tool's moderate complexity (search functionality with 2 parameters), no annotations, and no output schema, the description is largely complete for its core purpose. It explains what the tool does, when to use it, and the return format, but could improve by detailing output structure or error cases. It compensates well for the lack of structured data.

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 fully documents both parameters. The description adds no additional parameter-specific information beyond what's in the schema (e.g., it doesn't elaborate on query formatting or limit implications). Baseline score of 3 is appropriate since the schema handles parameter documentation effectively.

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 action ('Search the Pipeworx tool catalog') and resource ('tool catalog'), with explicit differentiation from sibling tools (which are all network-related). It uses precise language like 'by describing what you need' and 'Returns the most relevant tools with names and descriptions' to define its unique function.

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

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

The description provides explicit guidance on when to use this tool ('Call this FIRST when you have 500+ tools available and need to find the right ones for your task'), including a clear condition (500+ tools) and purpose (finding tools for a task). It implicitly distinguishes from sibling tools by focusing on tool discovery rather than network operations.

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

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

With no annotations, the description fully discloses behavior: it bundles multiple sources, returns pipeworx:// URIs, supports only company type, and expects ticker/CIK but not names (with instruction to use resolve_entity). This is transparent about limitations and capabilities.

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 with four sentences, each adding essential information. It front-loads the purpose, then details the data sources, return type, and usage guidance. No wasted words, and the structure is 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?

For a tool that aggregates multiple data sources, the description covers purpose, parameter usage, limitations, and alternatives. While there is no output schema, the mention of citation URIs gives a high-level understanding of return structure. Could benefit from more detail on the response format, but overall provides solid context for an agent to use the tool effectively.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaningful context beyond the schema by explaining how the value parameter should be used (ticker or CIK), that names are not supported, and the output includes citation URIs. This enhances usability beyond bare schema descriptions.

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

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

The description clearly states it returns a full entity profile across multiple Pipeworx packs in one call, listing specific data sources (SEC, XBRL, patents, news, LEI) and mentioning citation URIs. It differentiates from sibling tools by noting that for federal contracts, usa_recipient_profile should be used.

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

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

Explicitly states when to use (replaces 10-15 sequential calls) and when not to use (for federal contracts, call usa_recipient_profile directly). Provides clear alternative and context for efficient tool 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 states the tool deletes a memory, implying a destructive mutation, but fails to describe critical behaviors like whether deletion is permanent, requires specific permissions, or returns confirmation. This leaves significant gaps in understanding the tool's impact.

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, efficient sentence with zero wasted words. It is front-loaded with the core action and resource, making it immediately understandable without unnecessary elaboration.

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 destructive nature (deletion), no annotations, and no output schema, the description is incomplete. It doesn't address behavioral aspects like error handling, return values, or safety considerations, which are crucial for a mutation tool. This leaves the agent with insufficient context for reliable use.

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

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

The schema description coverage is 100%, with the parameter 'key' fully documented in the schema as 'Memory key to delete'. The description adds no additional meaning beyond this, such as key format or examples, so it meets the baseline score when the schema handles parameter documentation adequately.

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

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

The description clearly states the action ('Delete') and resource ('a stored memory by key'), which is specific and unambiguous. However, it doesn't explicitly differentiate this tool from its sibling 'recall' (which likely retrieves memories) or other tools, preventing a perfect score.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'recall' (for retrieval) or other memory-related operations. It lacks context about prerequisites, such as whether the key must exist or what happens if it doesn't, leaving usage unclear.

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 that the tool returns live data, network name, and station details (availability, slots, coordinates), which is useful behavioral context. However, it lacks information on error handling, rate limits, authentication needs, or data freshness, leaving gaps for a tool that fetches real-time data.

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 efficiently conveys the tool's purpose, parameter usage, and return data. Every part earns its place, with no redundant or unnecessary information, making it highly concise and front-loaded.

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

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

Given the tool's moderate complexity (fetching live data with one parameter) and no annotations or output schema, the description is adequate but incomplete. It covers the basic purpose and return structure, but lacks details on error cases, data formats, or operational constraints, which are important for a real-time data 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 description coverage is 100%, so the schema already documents the 'id' parameter well with examples. The description adds marginal value by reinforcing the parameter's purpose ('by its id') and providing context on what the id represents (e.g., network identifiers), but does not significantly enhance semantics 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 ('Get'), resource ('live station data for a bike-sharing network'), and specific scope ('by its id'). It distinguishes from sibling tools like 'list_networks' (which likely lists networks) and 'search_networks' (which likely searches networks) by focusing on retrieving detailed station data for a specific network.

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 'by its id', suggesting this tool is for when you already know the network identifier. However, it does not explicitly state when to use this tool versus alternatives like 'list_networks' or 'search_networks', nor does it mention prerequisites 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?

With no annotations provided, the description carries the full burden. It discloses the tool's behavior by specifying it returns data (name, id, location) and implies a read-only operation, but lacks details on potential limitations like rate limits, pagination, or error handling. The description adds basic context but misses richer 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 two concise sentences with zero waste: the first states the action and scope, and the second specifies the return data. It's front-loaded with the core purpose and efficiently structured.

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

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

Given the tool's low complexity (0 parameters, no annotations, no output schema), the description is reasonably complete. It covers what the tool does and what it returns, though it could benefit from more behavioral context (e.g., data freshness, limitations). The lack of output schema is partially compensated by describing return values.

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

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

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, focusing instead on the tool's purpose and output. This meets the baseline for tools with no 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 specific action ('List all bike-sharing networks worldwide') and resource ('bike-sharing networks'), with explicit scope ('worldwide'). It distinguishes from sibling tools by focusing on comprehensive listing rather than retrieval (get_network) or filtered searching (search_networks).

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 through 'List all... worldwide' and the return data format, suggesting this is for obtaining a complete global overview. However, it doesn't explicitly state when to use this tool versus alternatives like search_networks for filtered results or get_network for detailed information on a specific network.

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

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

Discloses rate limit (5 messages per identifier per day) and states it is free. No annotations provided, so description carries burden; adequate for a simple send operation with no side effects described.

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?

Very concise: two sentences that front-load the purpose and immediately provide usage context. No wasted words.

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

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

For a simple feedback tool, the description covers purpose, usage, inputs, and constraints (rate limit, do not include prompts). No output schema needed; complete for the task.

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 detailed parameter descriptions. The tool description does not add additional parameter semantics beyond the schema; baseline score applies.

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

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

Clearly states it sends feedback to the Pipeworx team and lists specific use cases (bug reports, feature requests, missing data, praise). Distinguished from sibling tools that perform queries or data 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?

Explicitly states when to use the tool and what not to include (end-user prompt verbatim). Mentions rate limit (5 per day). Does not explicitly exclude other tools, but siblings are unrelated.

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 clearly describes the tool's dual behavior (retrieve by key vs. list all) and persistence across sessions ('saved earlier in the session or in previous sessions'). It doesn't mention error handling, performance characteristics, or authentication needs, but provides sufficient operational 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 zero waste. The first sentence states the dual functionality clearly, and the second provides usage context. Every word earns its place, and the most important information (what the tool does) is front-loaded.

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

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

For a simple retrieval tool with 1 optional parameter and no output schema, the description provides good context about functionality, usage, and persistence. It doesn't describe return format or error cases, but given the tool's simplicity and the schema's coverage, it's reasonably complete.

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

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

The schema has 100% description coverage, so the baseline is 3. The description adds meaningful semantic context by explaining the optional parameter's effect: 'omit key to list all keys' clarifies the dual functionality. This goes beyond the schema's technical documentation to explain behavioral implications.

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 with specific verbs ('retrieve', 'list') and resources ('previously stored memory by key', 'all stored memories'). It distinguishes from siblings like 'remember' (store) and 'forget' (delete) by focusing on retrieval operations.

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

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

The description provides explicit usage guidance: 'Use this to retrieve context you saved earlier in the session or in previous sessions.' It also specifies when to omit the key parameter ('omit key to list all keys') and distinguishes this from storage operations implied by sibling 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?

Discloses key behavioral traits: fan-out to multiple sources in parallel, accepted date formats (ISO or relative), and output structure (structured changes, total_changes count, pipeworx:// URIs). No annotations are provided, so the description carries the full burden. Minor gaps: no mention of rate limits, error handling, or limits on results.

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 no wasted words. Front-loaded with the core purpose, then details on behavior, date formats, and output. Perfectly sized for quick parsing.

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

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

Covers the major aspects: entity type, date format, data sources, output structure. No output schema exists, so the description effectively explains the return value. Lacks details on result limits or performance considerations, but is complete for typical change-monitoring use cases.

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 description adds value beyond the schema by explaining the parallel fan-out behavior for 'type=company' and the purpose of 'since' and 'value' with examples. This enriches the agent's understanding beyond the basic parameter descriptions.

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

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

The description clearly states the tool's purpose: 'What's new about an entity since a given point in time.' It specifies the supported type ('company') and lists the data sources (SEC EDGAR, GDELT, USPTO), distinguishing it clearly from siblings like 'entity_profile' or 'compare_entities'.

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

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

Explicitly states when to use: 'Use for "brief me on what happened with X" or change-monitoring workflows.' It doesn't specify when not to use or alternatives, but the context is clear enough for the agent to select it appropriately.

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 adds valuable context beyond basic functionality: it explains persistence differences ('Authenticated users get persistent memory; anonymous sessions last 24 hours'), which is critical for understanding data retention. However, it does not cover other behavioral aspects like error conditions, rate limits, 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?

The description is appropriately sized and front-loaded: the first sentence states the core purpose, and subsequent sentences add essential context without redundancy. Every sentence earns its place by providing distinct information (e.g., usage examples, persistence details), with zero waste or unnecessary elaboration.

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

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

Given the tool's moderate complexity (2 required parameters, no output schema, no annotations), the description is mostly complete. It covers purpose, usage, and behavioral context (persistence rules), but lacks details on return values or error handling. Since there is no output schema, some gaps remain in explaining what happens after 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 description coverage is 100%, so the schema already documents both parameters ('key' and 'value') with examples. The description does not add any parameter-specific details beyond what the schema provides, such as constraints or usage tips. Baseline 3 is appropriate when the schema handles parameter documentation adequately.

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 action ('Store a key-value pair') and resource ('in your session memory'), distinguishing it from sibling tools like 'recall' (likely for retrieval) and 'forget' (likely for deletion). It provides concrete examples of what can be stored ('intermediate findings, user preferences, or context across tool calls'), making the purpose explicit and differentiated.

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

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

The description provides clear context on when to use this tool ('to save intermediate findings, user preferences, or context across tool calls'), but does not explicitly mention when not to use it or name alternatives (e.g., 'recall' for retrieval). It implies usage scenarios effectively, though lacks explicit exclusions or comparisons to siblings.

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

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

No annotations provided, so description carries full burden. Discloses accepted inputs (ticker, CIK, name), output fields (ticker, CIK, company name, URIs), and version limitation. Lacks error behavior or auth requirements, but sufficiently transparent for typical use.

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

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

Three tightly focused sentences: purpose, input details, output and benefits. No redundancy, 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?

For a simple two-param tool with no output schema, the description covers purpose, input formats, output, and value proposition. Minor omission: no mention of partial name matching or error handling, but sufficient for confident use.

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

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

Schema has 100% description coverage, baseline 3. Description adds value by explaining the relationship between type and value, providing examples, and noting return fields. Brings practical clarity beyond schema.

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

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

Clearly states the tool resolves an entity to canonical IDs in a single call. Identifies the specific resource (entity across Pipeworx data sources) and distinguishes from alternative multi-call approaches.

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

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

Explicitly says when to use: 'in a single call' and 'Replaces 2–3 lookup calls.' Provides input formats for v1. Could mention when not to use or compare to siblings like ask_pipeworx, but current guidance is 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 provided, the description carries full burden for behavioral disclosure. It mentions the search returns matching networks with location info, but doesn't address important aspects like whether this is a read-only operation, potential rate limits, authentication needs, error conditions, or pagination behavior for large result sets.

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 appropriately concise with two sentences that directly state the tool's function and what it returns. It's front-loaded with the core purpose and wastes no words.

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

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

For a simple search tool with one parameter and no output schema, the description provides basic context about what the tool does and returns. However, it lacks important behavioral details (especially given no annotations) and doesn't help differentiate from sibling tools, leaving gaps in 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 description coverage is 100%, so the schema already fully documents the single 'query' parameter. The description adds minimal value beyond what's in the schema - it mentions searching by city or country name, which the schema also specifies with examples.

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

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

The description clearly states the tool searches bike-sharing networks by location (city or country) and returns matching networks with location info. It specifies the verb 'search' and resource 'bike-sharing networks', but doesn't explicitly differentiate from sibling tools like 'get_network' or 'list_networks'.

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 about when to use this tool versus the sibling tools 'get_network' and 'list_networks'. The description implies usage for searching by location, but doesn't specify alternatives, exclusions, 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 full burden. It discloses the data sources (SEC EDGAR + XBRL), returns a verdict with extracted values, citations, and percent delta, and explains the supported claim types. No mention of destructive actions or authentication requirements, but for a fact-checking tool, this is sufficient.

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

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

The description is three sentences, each serving a distinct purpose: purpose and scope, components of the return value, and comparative value over alternatives. 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 has a single parameter and no output schema, the description provides enough context about what inputs are accepted and what outputs to expect. It could mention potential error cases or limitations (e.g., only US public companies), but the domain is already implied by 'SEC EDGAR + XBRL'. Overall, it is complete for the agent to use effectively.

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

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

The input schema has 100% description coverage for the single 'claim' parameter. The description adds example claims and implies the format, but the schema already defines the parameter adequately. Baseline 3 is appropriate.

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

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

The description uses specific verbs ('fact-check'), identifies the resource ('natural-language claim'), and specifies the supported domain ('company-financial claims via SEC EDGAR + XBRL'). It also lists the verdict types, which clearly distinguishes it from sibling tools.

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

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

The description states that the tool 'replaces 4–6 sequential agent calls', implying its efficiency advantage over manual multi-step processing. However, it does not explicitly mention when not to use it or name alternative tools.

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