Fintech Intel

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

No annotations provided, but description clearly discloses the tool's behavior: it selects the best data source, fills arguments, and returns results. Mentions it uses 'best available data source', which implies dynamic selection. However, lacks details on limitations or failure modes.

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 three sentences plus examples. Front-loaded with purpose and behavior. Examples add value but could be more tightly integrated.

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 only one parameter, no output schema, and no annotations, the description covers the tool's purpose, usage, and behavior well. It's sufficient for an agent to decide when to use this tool.

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

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

Only one parameter with schema coverage 100%. The description adds context that the question should be in plain English and can be requests or questions, which supplements the schema's 'Your question or request in natural language'.

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 plain English questions and returns answers by selecting the appropriate tool automatically. Provides concrete examples, distinguishing it from siblings which are specific 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?

Explicitly says to describe needs in natural language without browsing tools or learning schemas. Implicitly contrasts with siblings by advising 'no need to browse tools'. Examples show typical use cases.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the tool returns 'the most relevant tools with names and descriptions' and mentions default and max limit. However, it does not disclose any potential side effects, rate limits, or other behavioral traits such as whether it logs queries or requires authentication.

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, consisting of three short sentences. The key action and usage guidance are front-loaded, and every sentence adds value without redundancy.

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

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

Given the tool's moderate complexity (2 parameters, no output schema, no nested objects) and the absence of annotations, the description is fairly complete. It explains the tool's purpose, usage context, and parameter behavior. However, it lacks information about return format or error handling, which could be important for an agent to invoke 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?

The input schema has 100% coverage, with descriptions for both parameters. The description adds context about the 'limit' parameter (default 20, max 50) and provides examples for 'query' (e.g., 'analyze housing market trends'). This adds value beyond the schema by clarifying usage patterns and constraints.

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

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

The description clearly states the tool's purpose: 'Search the Pipeworx tool catalog by describing what you need.' It uses a specific verb ('Search') and resource ('Pipeworx tool catalog'), and distinguishes it from siblings by noting that it returns 'names and descriptions' for tool discovery, unlike other tools that perform specific fintech analyses or memory functions.

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: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This provides clear usage context and a directive to prioritize it before other tools, which effectively differentiates it from 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?

No annotations are provided, so the description must convey behavior. It implies a read-only lookup and lists data categories, but does not disclose potential latency, API limits, or whether results are cached. The description is adequate but lacks details on what happens if the bank is not found.

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

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

The description is concise (two sentences) and front-loaded with the tool's purpose, followed by supported data and input format. Every sentence adds value without 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 input schema (one string parameter) and no output schema, the description covers the core functionality well. It could be slightly improved by noting that the tool returns a report or summary, but it is sufficiently complete for an AI agent to understand its usage.

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% (one parameter described). The description adds context by specifying the parameter is a bank name and giving examples, which slightly exceeds the schema. However, no additional constraints (e.g., case sensitivity, partial name matching) are mentioned.

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 performs a bank health check, listing specific data sources (FDIC lookup, financials, complaints) and the required input (bank name). It distinguishes itself from siblings like 'fintech_company_deep_dive' and 'fintech_market_snapshot' by focusing on individual bank health.

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 (for bank health assessment) and provides an example input format. However, it does not explicitly state when not to use it or mention alternative tools like 'fintech_company_deep_dive' for broader company analysis.

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

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

With no annotations, the description carries the full burden. It mentions the data sources and that it requires a ticker, but does not disclose side effects (e.g., API call limits), rate limits, or whether results are cached. The description is factual but lacks depth on behavioral constraints.

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

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

The description is two sentences long and front-loaded with the main purpose. Every word adds value. It is concise and avoids 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 complexity (aggregating multiple data sources) and lack of output schema, the description is somewhat complete but omits details about the output format, pagination, or data freshness. It covers the inputs and scope adequately for a summary tool.

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

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

Schema description coverage is 100% so baseline is 3. The description does not add new parameter details beyond the schema, but it does imply that '_avKey' and '_fredKey' are optional and used for stock/macro data, which is already clear from the schema. No additional value added.

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 performs a 'complete company financial analysis' and lists the data sources (SEC filings, stock quote, etc.). It distinguishes itself from siblings like 'fintech_bank_health_check' and 'fintech_market_snapshot' by being comprehensive for a single company.

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

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

The description implies when to use it (for a full company analysis) and mentions providing a stock ticker, but does not explicitly state when not to use it or suggest alternatives for more specific needs (e.g., 'fintech_market_snapshot' for broader 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?

Annotations are empty, so description must cover behavioral traits. It discloses the optional FRED key for macro rates, but does not state if complaints/industry data require authentication, rate limits, or what happens on error. No contradiction with annotations.

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

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

The description is a single, well-structured sentence that front-loads the purpose and lists data sources concisely. No redundant 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, the description could mention what the dashboard returns (e.g., text summary, JSON), but it names key indicators. For a dashboard tool with one optional param, this is 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?

Schema coverage is 100%, so the description need not add much. It clarifies that _fredKey is optional and for macro rates, which matches the schema description. No additional meaning beyond schema, but baseline 3 is appropriate.

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

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

The description clearly states the tool aggregates CFPB, FDIC, and optionally FRED data, with specific metrics listed. It distinguishes itself from siblings like 'fintech_bank_health_check' and 'fintech_company_deep_dive' by focusing on a market-wide dashboard.

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

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

The description implies use for a broad market snapshot, contrasting with deeper dives on individual entities. It does not explicitly state when not to use or mention alternative tools, but the scope is clear from the listed data sources.

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 bears full burden. It states the tool deletes data, implying destructiveness, but does not clarify if the action is reversible, requires confirmation, or has side effects. Adequate 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 a single sentence of 6 words, with zero wasted text. It is front-loaded and efficient.

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, no nested objects) and lack of annotations, the description is adequate but minimal. It omits error behavior or return value information, which would be helpful.

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%, meaning the schema already describes the 'key' parameter well. The description adds no extra meaning beyond the schema, but this is acceptable given high coverage. Baseline 3 is elevated because the single parameter is self-explanatory.

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' uses a specific verb ('Delete') and resource ('stored memory by key'), clearly distinguishing the tool from siblings like 'recall' and 'remember'. It lacks explicit differentiation but is 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?

No guidance is given on when to use this tool vs. alternatives like 'recall' or 'remember'. There is no mention of prerequisites, such as whether the key must exist, or what happens if the key is not found.

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

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

Without annotations, the description carries the burden. It clearly states the tool is non-destructive (retrieval only) and explains behavior when key is omitted. No contradictions with missing annotations.

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, no wasted words. Purpose and usage are front-loaded. Every sentence adds value.

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

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

Given the tool is simple (one optional parameter, no output schema), the description is complete enough. It explains what happens with and without the key, and mentions persistence across sessions.

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% for the single parameter 'key'. The description adds value by explaining the effect of omitting the parameter (list all), which is not in the schema description that only says 'omit to list all keys'.

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 if key is omitted, with a specific verb 'retrieve' and resource 'memory'. It distinguishes itself from sibling tools like '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 explicitly states when to use (retrieve context saved earlier) and provides guidance on omitting key to list all. It doesn't explicitly mention when not to use or alternatives, but the context is clear given the 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?

With no annotations, the description carries full burden. It discloses persistence behavior: authenticated users get persistent memory, anonymous sessions last 24 hours. This adds context beyond the schema, but could mention memory limits or overwrite behavior.

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

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

Two sentences: first states purpose and verb, second gives usage context and persistence details. No wasted words, 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 simple tool (2 params, no output schema), the description is complete enough. It explains when to use and persistence behavior. It does not need to describe return values as there is no output schema.

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

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

Schema description coverage is 100%, so the schema fully describes both parameters. The description adds a usage context ('save intermediate findings...') but does not add meaning beyond the schema's 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 it stores a key-value pair in session memory, using specific verbs ('Store') and a concrete resource ('session memory'). It distinguishes from siblings like 'forget' and 'recall' by its purpose.

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 when to use this tool: to save intermediate findings, user preferences, or context across tool calls. It implies alternatives (recall/forget) but does not explicitly exclude them or provide when-not-to-use guidance.

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