Tinder Bio

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

Describes behavior as picking the right tool and filling arguments, which goes beyond annotations (none provided). However, does not detail limitations (e.g., data freshness, latency, or when it might fail). Still, the description is honest about the autonomous routing.

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

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

Concise at 4 sentences. Front-loaded with the core purpose. Examples are helpful but could be trimmed to 1-2. No wasted words.

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

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

Given the tool's simplicity (1 param, no output schema) and rich annotations (none), the description is largely complete. It explains the autonomous behavior and gives examples. Could mention that it may not handle domain-specific jargon or that answers depend on available tools.

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

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

Schema coverage is 100% with only one parameter 'question'. The description adds context by explaining it's natural language and providing examples, but the schema already describes it well. Baseline 3 is appropriate.

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

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

Clearly states the tool accepts a plain English question and returns an answer from the best data source. Distinguishes itself from siblings by acting as a natural language router, unlike others like discover_tools or tinder_bio_generate.

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 tells when to use: 'just describe what you need', implying it's for broad questions. Provides concrete examples, making it clear this is the go-to for quick answers without needing to browse other tools.

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

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

With no annotations, description transparently discloses it returns paired data and resource URIs from specific sources (SEC EDGAR, FDA, clinical trials). No mention of side effects or auth needs, but sufficient for a read-only comparison 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?

Two sentences total: first states purpose and types, second adds return info and efficiency gain. No unnecessary words; every sentence earns its place.

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

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

Given the complexity of comparing 2–5 entities and no output schema, description adequately covers data sources and return types. However, lacks details on 'paired data' format and example URIs, which could further enhance 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?

Beyond 100% schema coverage, description adds concrete examples for each parameter (e.g., tickers vs drug names) and clarifies the allowed entity types and value formats, significantly aiding parameter understanding.

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

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

Description uses specific verb 'Compare' and resource 'entities', clearly differentiating from siblings like 'resolve_entity'. Defines two entity types with distinct data fields, which is precise and 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?

Explicitly states it replaces 8–15 sequential calls, implying efficiency for bulk comparisons. Clear context for when to use (comparing multiple companies/drugs), but lacks explicit 'when not to use' or alternatives beyond 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?

Without annotations, description must carry behavioral burden. It discloses that the tool returns 'most relevant tools with names and descriptions' and that it's for a catalog of 500+ tools. However, it doesn't specify whether the tool is read-only or has side effects, though search is inherently safe. Slight gap but still good.

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

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

Three sentences, each earning its place: purpose, return value, and when to use. No wasted words.

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

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

Given no output schema and no annotations, the description adequately covers purpose and usage. Missing details like exact result format or pagination, but for a search tool with simple schema, this is sufficient.

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

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

Schema coverage is 100%, so baseline is 3. The description adds little beyond schema: it repeats the query example pattern and mentions default/max limit. No extra semantic value beyond what the schema 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?

Description clearly states the tool searches a tool catalog by natural language query and returns relevant tool names and descriptions. The verb 'search' and resource 'tool catalog' are specific, and it distinguishes from siblings by being the only search/discovery tool among them.

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

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

Explicitly says 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This provides clear when-to-use guidance and implies the alternative is browsing or guessing, making it a recommended first step.

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

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

Given no annotations, the description discloses that the tool returns pipeworx:// citation URIs and bundles multiple sources. However, it lacks details on potential errors, authentication requirements, or performance implications, which would be useful for an 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 two sentences, well-structured with purpose first, then details and alternatives. Every sentence adds value, no redundancy.

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

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

For a complex tool bundling multiple data sources, the description explains what is returned (citation URIs) and provides alternatives. It lacks details on the return structure or error handling, but overall is fairly complete.

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

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

Schema coverage is 100% and the description adds significant context: 'type' is limited to 'company', and 'value' explains allowed formats (ticker or CIK) with a note that names are not supported, directing to resolve_entity.

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

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

The description clearly states the tool returns a full profile of an entity across multiple data sources (SEC, XBRL, USPTO, GDELT, LEI) for type='company'. It distinguishes from sibling tool 'usa_recipient_profile' and implies it replaces many sequential calls.

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 when to use (comprehensive profile) and when not (federal contracts, use usa_recipient_profile). It also provides guidance to use resolve_entity when only a name is available.

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 states that the operation is deletion, which is inherently destructive, but does not mention reversibility, confirmation, or side effects. It is adequate but not detailed.

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

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

A single sentence of 6 words, front-loaded with the verb. Every word earns its place. No unnecessary information.

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

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

The tool has one simple required parameter, no output schema, and no nested objects. The description is sufficient for basic understanding, but could mention what happens if the key does not exist or if the deletion is successful.

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% (the only parameter 'key' is described as 'Memory key to delete'). The description adds no extra meaning beyond what the schema provides, so baseline 3 is appropriate.

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

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

The description 'Delete a stored memory by key' is a specific verb+resource combination. It clearly states what action is performed and on what, and distinguishes it from siblings like 'remember' (store) and 'recall' (retrieve).

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 by stating the action, but does not explicitly mention when to use this tool versus alternatives like 'remember' or 'recall'. However, given the sibling names, the distinction is clear without 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. It discloses the rate limit of 5 messages per identifier per day and gives content rules. This is sufficient for a feedback submission tool with no destructive or complex 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 three sentences long, front-loaded with the purpose, and every sentence adds value. There is 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 tool with 3 parameters and no output schema, the description covers the purpose, usage guidelines, rate limiting, and content rules. It does not describe return values, but that is unnecessary for a feedback submission tool. It is complete enough for an agent to use correctly.

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

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

While the input schema already describes all parameters (100% coverage), the description adds extra value by explaining how to structure the message (describe what was tried, avoid verbatim prompts) and clarifying enum values for the type parameter. This goes beyond the schema descriptions.

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

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

The description clearly states the verb 'Send feedback' and the resource 'to the Pipeworx team', listing use cases like bug reports, feature requests, missing data, or praise. It distinguishes this tool from siblings by focusing solely on feedback, unlike others that perform searches or tool discovery.

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 (bug reports, feature requests, etc.) and provides guidelines on what to include (Pipeworx tools/data context) and exclude (end-user's prompt verbatim). It also mentions rate limits. However, it does not explicitly state when not to use it or list alternatives, though no alternative exists among 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 are provided, so the description carries the full burden. It discloses that omitting the key lists all memories, which is a behavioral trait. However, it does not mention if retrieval is read-only, any side effects, or persistence across sessions beyond 'earlier in the session or in previous sessions'. Adequate but not exhaustive.

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 fluff. The first sentence states the core functionality; the second provides usage context. Every sentence earns its place.

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

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

For a simple tool with one optional parameter and no output schema, the description is complete enough. It explains both modes (by key and list all) and provides context about retrieving saved information. No major gaps.

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

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

Schema description coverage is 100% (the one parameter 'key' is described). The description adds the behavioral detail that omitting the key lists all memories, which is not in the schema. This adds value beyond the schema, so score is above baseline 3.

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

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

The description clearly states the tool retrieves a memory by key or lists all memories, with a specific verb ('retrieve') and resource ('stored memory'). It distinguishes itself from 'remember' and 'forget' by focusing on 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 explains when to use this tool ('to retrieve context you saved earlier') and implies the alternative ('omit key' to list all). It does not explicitly mention when not to use it or contrast with siblings like 'remember' or 'discover_tools', but the context is clear.

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

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

No annotations are provided, so the description carries full burden. It explains the parallel fan-out behavior, accepted input formats for 'since', and the return structure (structured changes, count, URIs). It lacks details on error handling or latency, but overall provides sufficient behavioral context.

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

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

The description is concise with no wasted words. It front-loads the core purpose, then efficiently explains fan-out, input formats, output structure, and usage suggestion. Every sentence adds essential information.

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

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

Given the tool's moderate complexity, the description covers the key aspects: fan-out to sources, input parameters, output structure. It lacks discussion of error responses or authentication requirements, but it is largely complete for its intended use case.

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 good parameter descriptions. The description adds value by clarifying the 'since' formats (ISO date vs relative), the 'value' formats (ticker or CIK), and the 'type' enum. This extra context compensates for the high schema coverage, justifying a score above baseline 3.

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

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

The description clearly states the tool's function: 'What's new about an entity since a given point in time.' It specifies the supported entity type (company) and details the fan-out to multiple sources (SEC, GDELT, USPTO), distinguishing it from sibling tools 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?

The description explicitly tells when to use the tool: 'Use for "brief me on what happened with X" or change-monitoring workflows.' While it does not explicitly state when not to use or mention alternatives, the context with sibling tools implies appropriate usage.

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

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

The description discloses persistence behavior (authenticated vs 24-hour TTL), which adds value beyond the absence of annotations. However, it does not mention idempotency, size limits, or whether overwriting is allowed. With no annotations, this is adequate but not exhaustive.

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, each adding distinct value: the first defines the action, the second explains use cases and persistence. No filler or redundancy.

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

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

Given the simple key-value operation with 100% schema coverage and no output schema, the description adequately covers purpose, usage, and persistence. It lacks explicit mention of return value or error cases, but these are minimal concerns for a straightforward tool.

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

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

Schema coverage is 100%, so the schema already documents both parameters. The description adds no extra semantic detail beyond what the schema provides (e.g., key and value descriptions). Baseline 3 is appropriate as the description does not enhance parameter understanding.

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

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

The description explicitly states 'Store a key-value pair in your session memory', which is a specific verb+resource combination. It clearly differentiates from sibling tools like 'recall' (retrieve) and 'forget' (delete), making the purpose unambiguous.

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

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

The description provides clear usage guidance: 'save intermediate findings, user preferences, or context across tool calls'. It distinguishes between authenticated and anonymous sessions, which is useful context, but does not explicitly mention when not to use it or suggest alternatives.

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

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

No annotations provided, so description carries full burden. It describes functionality and output but lacks information on error handling, rate limits, case sensitivity, or side effects. Adequate for a read-only lookup, but could be more 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 two sentences, front-loaded with purpose, and every sentence adds value. No wasted words.

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

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

For a simple tool with 2 parameters and no output schema, the description covers inputs, output format, and benefit. It lacks mention of error scenarios or case sensitivity, but is otherwise complete.

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

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

Schema coverage is 100% with parameter descriptions. The description adds value by providing concrete examples for 'value' (ticker, CIK, name) and specifying the current type limitation, going beyond the schema.

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

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

The description clearly states the verb 'resolve', the resource 'entity to canonical IDs', and the context 'across Pipeworx data sources in a single call'. It distinguishes from siblings by noting it replaces 2-3 lookup calls.

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 specific usage details: v1 supports only type='company', accepts ticker/CIK/name, and returns canonical IDs. It implies efficiency but does not explicitly state when not to use or contrast with alternatives.

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