Jargon Translator

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

The description discloses key behavioral traits: it picks the right tool, fills arguments, and returns results. Since no annotations are provided, the description carries full burden, and it does a good job explaining what the tool does internally. However, it doesn't mention any potential limitations, rate limits, or error conditions.

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 (three sentences) and front-loaded with the core purpose. Every sentence adds value: first sentence states function, second explains how it works, third provides examples. 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 has only one required parameter and no output schema, the description adequately covers what the agent needs to know: it's a natural language query interface. It is complete enough for a simple tool, though it could mention that the answer is returned as text.

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 baseline is 3. The description adds meaning by clarifying that the parameter 'question' is a natural language request, and provides examples, but it does not go beyond what the schema already states.

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 answers plain English questions by automatically selecting the best data source, filling arguments, and returning results. It specifies the verb 'ask' and the resource 'Pipeworx' and distinguishes itself from sibling tools like discover_tools or jargon_translator_translate by emphasizing natural language queries.

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: when you want to ask a question in plain English without browsing tools or learning schemas. It gives examples that illustrate use cases, but does not explicitly say when not to use it or mention 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 discloses return data (paired data + pipeworx:// URIs) and data sources (SEC EDGAR, FDA). It does not mention rate limits or auth needs, but for a read-only comparison tool, core behavioral traits are sufficiently covered.

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

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

Two sentences, zero waste. The first sentence instantly conveys the core purpose and scope (2-5 entities, side-by-side), and the second provides type-specific details and efficiency benefit. Ideal structure.

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, description adequately explains return data (paired data + URIs). It covers both entity types with specific metrics. Missing details like error handling or pagination, but for a moderate-complexity tool, it is sufficiently complete for an agent to use correctly.

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

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

Input schema has 100% coverage for both parameters. Description adds meaning beyond schema by listing the specific financial metrics for company type (revenue, net income, cash, long-term debt) and drug metrics (adverse-event count, FDA approval count, active trial count), enriching the agent's 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 clearly states it compares 2-5 entities side by side with distinct metrics per type (company vs drug), explicitly naming the data sources (SEC EDGAR, adverse-event reports). This distinguishes it from sibling tools like resolve_entity or ask_pipeworx, which are single-entity or general query 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?

States it replaces 8-15 sequential calls, implying efficiency for multi-entity comparisons. While it does not explicitly list when not to use it, the sibling tools are clearly different (memory, single entity, discovery), so context is clear. Could benefit from explicit exclusions.

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

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

No annotations are provided, so the description carries the burden. It mentions that the tool 'returns the most relevant tools with names and descriptions', which is helpful. However, it does not disclose behavior like whether the search is based on vector similarity or keyword matching, or any limitations. The description 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?

The description is only three sentences, each providing essential information: purpose, what it returns, and when to use it. No wasted words, 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 low complexity (2 parameters, no output schema, no nested objects), the description is complete. It covers purpose, usage, and parameter details sufficiently for an agent to invoke it correctly.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds value by explaining the 'query' parameter's usage with examples ('analyze housing market trends', etc.) and clarifies the 'limit' parameter's default and max. This goes beyond the schema's description, warranting a 4.

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

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

The description clearly states the verb 'Search' and the resource 'Pipeworx tool catalog', and specifies the purpose: finding the right tools for a task. It also distinguishes itself from siblings by being the tool to call first when many tools are available.

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 'Call this FIRST when you have 500+ tools available', providing clear when-to-use guidance. It also implies that other tools like ask_pipeworx, forget, etc., are for different purposes, though alternatives are not explicitly listed.

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 bears the full burden. It states 'Delete' implying mutability, but does not disclose side effects, irreversibility, or error conditions. The behavior is minimally 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?

The description is a single, clear sentence with no wasted words. It is appropriately concise for a simple tool with one parameter.

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), the description is minimal. It lacks information about return value, error states, or confirmation, which would be helpful for an 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?

Schema coverage is 100%, so the schema already documents the single parameter. The description adds 'by key' confirming the parameter's role, but does not elaborate on format or constraints 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 ('Delete') and the resource ('a stored memory by key'). It distinguishes itself from siblings like 'recall' (read) and 'remember' (write), but could be more explicit about the uniqueness of 'forget' 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?

No guidance is provided on when to use this tool vs. alternatives. It does not mention prerequisites (e.g., memory must exist) or cases where deletion might fail, leaving the agent to infer usage from 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?

Despite no annotations, the description discloses the rate limit ('5 messages per identifier per day. Free.') and advises on appropriate content, providing transparency about behavioral constraints. It does not mention any destructive actions, which is expected for a feedback 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 extremely concise (two sentences) and well-structured: it starts with the core purpose, then lists use cases, followed by content guidelines and rate-limit info. 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?

The description fully covers purpose, usage, content guidelines, rate limits, and parameter hints. No output schema is needed for a feedback tool, and the missing information is not required for effective 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?

With 100% schema coverage, the description adds value by guiding on message content ('Describe what you tried...' and 'do not include the end-user's prompt'), complementing the schema's type and context descriptions. This enhances 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 clearly states 'Send feedback to the Pipeworx team' and enumerates specific use cases (bug reports, feature requests, missing data, praise), making the purpose unambiguous and distinct from sibling tools which focus on 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?

The description provides explicit guidance on what to include ('Describe what you tried in terms of Pipeworx tools/data') and what to avoid ('do not include the end-user's prompt verbatim'), along with rate-limit information. It does not explicitly state when not to use the tool, but the use cases are clear enough.

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 clearly states retrieval behavior and that omitting key lists all, which is sufficient for a non-destructive read 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?

Two clear sentences, front-loaded with verb and resource, 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 single optional parameter, no output schema, and simple read operation, description is complete enough. It covers the key behavior and the list-all fallback.

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 schema already describes 'key' as memory key to retrieve. Description adds nuance that omitting key lists all, but this is already implied by non-required parameter. 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 retrieves a memory by key or lists all if key omitted, distinguishing it from 'remember' (store) and 'forget' (delete).

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 to use for retrieving context saved earlier, and implies omitting key to list all. No explicit alternatives or when-not-to-use, but sibling names clarify roles.

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 users get persistent memory, anonymous sessions last 24 hours. No annotations are provided, so the description compensates well for this gap.

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 adding value: first sentence defines the action, second gives use cases, third discloses persistence. 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 is simple with only two required string params and no output schema. The description covers purpose, usage, and behavioral notes. It could mention that memory is per-session and that keys must be unique, but it's largely 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 good descriptions for both parameters. The description adds little beyond the schema; it implies what values might be stored (findings, addresses) but doesn't add constraints or formatting 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 clearly states the tool stores a key-value pair in session memory, distinguishing it from sibling tools like 'recall' (retrieve) and 'forget' (delete). The verb 'store' and resource 'key-value pair' are specific.

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

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

The description gives explicit usage contexts: save intermediate findings, user preferences, or context across tool calls. It doesn't explicitly state when not to use it or name alternatives, but the sibling context provides some differentiation.

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, but the description discloses it is a single-call lookup, returns specific fields, and implies no side effects. It does not mention authentication or rate limits, but for a read-like operation, 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?

Two sentences with a bullet-like list; no wasted words. Purpose is front-loaded, and all information is relevant and compact.

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

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

Given no output schema, the description adequately explains the return values (ticker, CIK, company name, URIs) and covers all aspects of the tool's use: type, input formats, and purpose.

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. Description adds value by providing concrete examples (e.g., 'AAPL', '0000320193', 'Apple') and explaining the output structure, going beyond the schema.

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

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

The description clearly states it resolves entities to canonical IDs, specifies supported type (company), input formats (ticker, CIK, name), and outputs (ticker, CIK, company name, URIs). It distinguishes from alternatives 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 indicates when to use the tool (to resolve entities in a single call) and what it replaces, but does not explicitly state when not to use it or compare to sibling tools. However, given no direct competitor, it is adequate.

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