Alphavantage

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

The description discloses that Pipeworx picks the right tool and fills arguments, indicating autonomous decision-making. Since no annotations are provided, the description carries the full burden, and it does well but could mention limitations like potential latency or scope of data sources.

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 three sentences: function, mechanism, examples. It is front-loaded and efficient, with 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 simple input schema (one string parameter) and no output schema, the description is complete enough. It explains the tool's behavior and provides usage guidance, leaving no critical 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?

The schema provides 100% coverage for the single 'question' parameter, and the description adds meaning by explaining it as natural language input and giving examples. A slight deduction for not specifying the expected format or constraints on question length.

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: to answer plain English questions by selecting the best data source and filling arguments. It distinguishes itself from sibling tools by offering a unified, natural language interface that abstracts away individual tool details.

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 'no need to browse tools or learn schemas' and provides example questions, guiding the agent to use this tool when the user asks broad or ambiguous queries rather than specifying a particular tool.

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

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

The description indicates it returns both annual and quarterly reports, which is useful behavioral context. No annotations are provided, so the description carries full burden; it does not disclose side effects, rate limits, or data freshness.

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 at two sentences, front-loaded with purpose and then listing included data. 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 only 2 well-documented parameters and no output schema, the description adequately explains what data is returned. Could mention that data is historical or that reports cover multiple periods.

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 does not add additional meaning beyond what the schema provides, so baseline score of 3 is appropriate.

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

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

The description clearly states it retrieves balance sheet data for a company, including both annual and quarterly reports, and lists key financial items such as assets, liabilities, equity, cash, and debt. This distinguishes it from sibling tools like av_income_statement and av_earnings.

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 it should be used for balance sheet data but does not explicitly state when to use it versus alternatives (e.g., av_income_statement for income data). No guidance on prerequisites or when not to use.

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 the description carries full burden. It discloses the data fields returned, the two output sizes, and the time ranges (100 days vs 20+ years). It does not mention rate limits, API key usage details, or whether data is adjusted for splits/dividends, but the core behavioral traits are well-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?

The description is two sentences long, front-loaded with the core action and data fields. It wastes no words, though it could arguably be more concise by combining the two sentences. The structure is clear 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 complexity (3 params, no output schema, no annotations), the description is largely complete. It explains the purpose, data fields, and output size options. However, it lacks details on the format of the returned data (e.g., JSON structure, date format) and any caveats like adjusted vs unadjusted prices. Still, it provides enough 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 description coverage is 100%, so baseline is 3. The description adds context by specifying that outputsize controls the number of data points and the time range, which is already partially in the schema but adds the '20+ years' detail. It does not elaborate on symbol or _apiKey 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 specifies a verb ('Get') and a resource ('daily time series for a stock'), and distinguishes itself from siblings by mentioning the data fields (open, high, low, close, volume) and the time range options. Siblings like av_quote or av_overview serve different purposes, and the description makes the unique role clear.

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 the default behavior (100 days) and the alternative (full history), but does not explicitly tell the agent when to use this tool versus alternatives like av_quote (which likely gives a single data point). There is no direct mention of when not to use it or which sibling to prefer, though the purpose is clear enough to infer.

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 carries full burden. It describes the return content (EPS, surprise) but doesn't mention if data is historical or current, API rate limits, or any side effects. For a read-only tool, this is adequate but minimal.

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

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

Single sentence with clear structure, front-loads key action (Get earnings data) and enumerates data types concisely. 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?

For a tool with no output schema and empty annotations, the description covers the purpose and output shape adequately. However, it lacks context on data freshness, pagination, or error conditions, which could be important 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% and both parameters are described. The description adds value by explaining what earnings data is returned (annual/quarterly EPS, surprise), complementing the schema's field 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 retrieves earnings data, specifying EPS types (reported/estimated), surprise metrics, and granularity (annual/quarterly). It distinguishes itself from siblings like av_income_statement and av_overview which focus on different financial data.

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 on when to use this tool vs siblings. For example, it doesn't explain that av_overview might provide summary financials while this focuses on earnings specifics. No alternatives or exclusions mentioned.

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 cover behavioral traits. It explains that the tool returns both annual and quarterly reports, which is helpful, but it does not disclose potential rate limits, authentication requirements (beyond the _apiKey parameter), or data freshness. The description 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 sentences, front-loaded with the core purpose and then listing key metrics. No wasted words, but could be slightly more structured with bullet points for readability. Still concise and effective.

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 provides a reasonable overview. However, it lacks details about time range options, data format, or handling of missing data. The tool is relatively simple, so completeness is adequate but not comprehensive.

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

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

Schema coverage is 100% with descriptions for both parameters (symbol and _apiKey). The description adds context about what data is returned but does not enhance parameter meaning beyond the schema. 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 gets income statement data for a company, including both annual and quarterly reports. It lists specific metrics (revenue, gross profit, operating income, net income, EBITDA) and the resource is well-differentiated from siblings like av_balance_sheet.

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

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

The description implies usage for retrieving income statement data but does not explicitly state when to use this vs siblings like av_earnings or av_overview. No exclusion criteria or alternatives are mentioned, leaving the agent to infer based on the tool name and sibling list.

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

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

No annotations provided, so description carries full burden. It states the tool returns a set of fundamental data fields, which is clear. However, it does not disclose any side effects, data freshness, rate limits, or API key usage implications beyond the input schema.

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

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

Single sentence listing key outputs. Efficient and front-loaded. No redundant 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 there is no output schema, the description partially compensates by listing typical fields returned. However, it does not specify the structure (e.g., JSON keys), or handle edge cases like invalid symbol. Adequate but not comprehensive.

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

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

Schema coverage is 100% with descriptions for both parameters (symbol and _apiKey). Description adds a brief list of returned fields, which provides some context but does not elaborate on parameter behavior (e.g., accepted formats for symbol). 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?

Description clearly states it gets company overview and fundamentals, listing specific data points (description, sector, market cap, P/E ratio, EPS, dividend yield, 52-week range). It distinguishes from siblings like av_quote (which likely returns current price only) and av_balance_sheet/av_income_statement (which focus on financial statements). However, it does not explicitly differentiate from siblings.

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?

Description implies it is used to get fundamental data for a stock symbol, but does not provide when-to-use vs alternatives. No explicit guidance on when not to use or which sibling to choose instead.

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

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

No annotations are provided, so the description must carry the full burden. It correctly states it returns a real-time quote, but does not disclose rate limits, API key requirements beyond the schema, or what happens on errors. Schema covers parameters but behavioral details are missing.

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, concise sentence that effectively communicates the tool's purpose and return fields. No unnecessary words or repetition.

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

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

Given the tool has no output schema and few parameters, the description provides adequate high-level information about the tool's purpose. However, it lacks details on expected output format or any limitations, which would be valuable for an agent to fully understand 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?

The input schema has 100% coverage, documenting both 'symbol' and '_apiKey' with clear descriptions. The description does not add any additional meaning beyond what the schema already provides, so a baseline score of 3 is appropriate.

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

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

The description specifies a clear action ('get a real-time stock quote') and lists the key data fields returned (price, change, change percent, volume, latest trading day). It uniquely identifies its purpose among sibling tools like 'av_daily' and 'av_overview', which serve different 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 implies use when a quick current quote is needed, but does not explicitly state when to use alternatives like 'av_daily' or 'av_overview'. No guidance on prerequisites or 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 set readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds significant behavioral context: it explains the fan-out logic (e.g., 'crypto+fred+gdelt for a BTC bet'), classification into bet types, and the return of a 'market-vs-model comparison'. This aligns with annotations and enriches understanding beyond them.

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 paragraph that is dense with information but still well-structured. Every sentence adds value, and the core action is front-loaded. However, it could be slightly shortened without losing meaning, e.g., combining some sentences. It remains very effective.

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?

Despite no output schema, the description clearly states what is returned: 'an evidence packet plus a simple market-vs-model comparison'. It covers the input flexibility, the processing steps, and the intended use. For a complex tool, this is 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 both parameters described. The description adds meaningful context: for 'market', it gives examples ('"will-bitcoin-hit-150k-by-june-30-2026"') and explains it can accept a URL or question text. For 'depth', it clarifies the default ('thorough') and the trade-off ('quick = 2-3 evidence sources'). This exceeds what the schema alone provides.

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

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

The description clearly states the tool's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies input types (slug, URL, question text), the actions performed (resolves, classifies, fans out), and the output (evidence packet with market-vs-model comparison). It distinguishes itself from sibling tools by being the central bet research tool.

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

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

Explicit usage scenarios are provided: 'Use for "should I bet on X?", "what does the data say about this Polymarket market?", or "is there edge in this bet?"' This gives clear guidance on when to invoke the tool. Although it does not mention when not to use, the context implies its unique role, and no sibling tool directly competes.

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 responsibility. It discloses the data sources (SEC EDGAR for companies, FDA and clinical trial databases for drugs) and the return format (paired data + pipeworx:// URIs). It implies a read-only operation with no destructive behavior. While it could mention pagination or rate limits, the behavioral disclosure is adequate.

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

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

The description is extremely concise—two sentences—with no filler. The first sentence immediately states the core purpose and accepted range of entities. The second sentence breaks down the two types and their outputs. Every word contributes to understanding.

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 there are only two parameters and no output schema, the description covers the essential aspects: what the tool does, what data it retrieves per type, and what it returns (paired data + URIs). It could mention error handling or what happens if an entity is not found, but for a comparison tool it is sufficiently 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 covers 100% of parameters with descriptions, but the description adds significant value by explaining how the 'values' parameter should be filled based on type (tickers/CIKs for company, drug names for drug) and what data fields each type returns. This enriches the schema beyond the basic 'string array' and 'enum' definitions.

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 compares 2–5 entities side by side, specifies two entity types (company/drug) with concrete data fields (revenue, net income, etc. for companies; adverse-event counts, FDA approvals, etc. for drugs). It distinguishes itself from sibling tools like av_balance_sheet or av_income_statement, which operate on single entities, by offering multi-entity comparison in one call.

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 it replaces 8–15 sequential agent calls, which strongly implies the tool is for multi-entity comparisons where otherwise many individual calls would be needed. It does not provide explicit when-not-to-use advice, but the context of sibling single-entity tools clarifies alternatives. The guidance is clear enough for most 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 are provided, so the description must disclose behavioral traits. It states it returns 'the most relevant tools with names and descriptions' and mentions default/max limits, but does not specify whether it is read-only or if it has side effects. Since it's a search tool, likely read-only, but not explicitly stated. Given no annotations, a 3 is appropriate.

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 adding value: first states the action, second states the return, third gives the call-first directive. No wasted words, 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?

Given the tool has only 2 simple parameters, no output schema, and no annotations, the description adequately covers the purpose, usage, and behavior. It could optionally mention that it's read-only or that results are sorted by relevance, but it is already fairly complete for its complexity.

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

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

Schema coverage is 100%, so both parameters are documented in the schema. The description adds no further meaning beyond the schema: it repeats the query parameter usage ('Natural language description') but does not elaborate on how results are ranked or how to structure queries for best results. Baseline 3 is correct.

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

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

The description clearly states it searches a tool catalog by a natural language query, returning relevant tool names and descriptions. It distinguishes itself from sibling tools (which are mostly data retrieval or memory tools) by being the tool discovery entry point.

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.' Provides a clear directive on when to use it and the context (many tools). No need for alternatives as it's the primary discovery mechanism.

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 must convey behavior. It explains the tool aggregates data from multiple sources into one call and returns citations as URIs. It lists included data types. Missing an explicit statement that it is read-only, but the context strongly implies no side effects. Overall, it provides good behavioral insight.

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 paragraph, front-loaded with the core purpose. Every sentence adds value: purpose, data list, efficiency claim, and citation format. 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, the description lists the categories of returned data (SEC, XBRL, patents, etc.) but does not detail the response format beyond URIs. It lacks specifics on size limits or pagination, but as a 'full profile' tool, completeness is high. Minor gap in structural details.

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 param descriptions. The description adds value by explaining that 'value' accepts ticker or CIK and that 'type' is currently limited to 'company', and it directs users to resolve_entity for name inputs. This goes beyond the schema and provides practical guidance.

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 what the tool does: returns a full profile of an entity across multiple packs. It lists specific data categories (SEC filings, XBRL, patents, etc.) and distinguishes itself as a composite tool that replaces many sequential calls, setting it apart from siblings like resolve_entity or av_overview.

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 to use this for a comprehensive profile and mentions that it replaces 15–30 sequential calls, indicating high-value use. It also guides when not to use it: if you only have a name, use resolve_entity first. However, it does not explicitly state when to prefer simpler alternatives if only one data type is needed.

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 full responsibility. It indicates a destructive action (deletion) but does not disclose whether the operation is irreversible, any authorization requirements, or side effects. Adequate but could add details like 'permanently deleted'.

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?

One sentence, zero waste. Perfectly concise for a single-parameter delete operation.

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 low complexity (1 param, no output schema, no nested objects), the description is nearly complete. It could mention that the key must match an existing memory, but that is implied.

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

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

Schema coverage is 100% with a single parameter 'key' described as 'Memory key to delete'. The description adds no extra meaning beyond the schema, so baseline 3 applies.

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

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

The description clearly states the verb 'Delete' and the resource 'stored memory by key'. It distinguishes from siblings like 'remember' and 'recall' by specifying deletion.

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 (to delete a memory), but provides no guidance on when not to use it or alternatives. Given sibling tools like 'forget', 'remember', and 'recall', usage context is clear but no exclusions are mentioned.

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 rate limiting ('5 messages per identifier per day') and states the tool is free. This addresses key behavioral traits for a feedback tool, though it omits mention of persistence or review process.

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 compact sentences plus a rate-limit note. Every sentence provides essential information, and the most critical details appear first. 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 tool's simplicity (3 parameters, no output schema) and the richness of the input schema, the description covers purpose, usage, and constraints. It could mention what happens after submission, but that is not strictly necessary for correct 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%, and the description reinforces usage (e.g., 'describe what you tried' for message) but does not add much new information beyond the schema's parameter descriptions. The baseline is 3 for high coverage, and the added context is marginal.

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', the resource 'feedback', and enumerates specific use cases: bug reports, feature requests, missing data, or praise. It uniquely distinguishes from siblings like ask_pipeworx or data tools, leaving no ambiguity.

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 the tool (for feedback) and provides formatting guidance ('describe what you tried... do not include end-user prompt verbatim'). While it doesn't specify when not to use it, the context makes it clear this is solely for feedback, and siblings cover other intents.

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 already declare read-only and non-destructive behavior. The description adds context beyond annotations: explains search and grouping logic, output format (ranked opportunities with reasoning), and mode 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?

Description is slightly long but efficiently structured. Front-loads purpose, then explains modes in a clear, scannable way. Every sentence adds value, though minor redundancy could be trimmed.

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 return value (ranked opportunities with reasoning). Covers both modes, usage guidance, and examples, making the tool fully understandable for an AI 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%, but description adds significant meaning: explains what each parameter does, gives example values, and clarifies the difference between the two modes. This goes well 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?

Clearly states the tool finds arbitrage opportunities on Polymarket via monotonicity checks. Describes two distinct modes (event and topic) with examples, differentiating from sibling tools like polymarket_edges.

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 explains when to use each mode: event for single-event checks, topic for cross-event arbitrage. Gives a concrete scenario where single-event mode fails and topic mode succeeds, providing clear guidance on 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?

Annotations already declare readOnly and non-destructive. Description adds detail: groups by asset, fetches price history ONCE, uses a lognormal model from FRED and coinpaprika. No 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?

Description is three sentences with clear structure. Some details like model sources could be trimmed, but overall efficient 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?

No output schema, but description explains return value: ranked by edge magnitude with suggested trade direction. Covers model and data sources adequately for the tool's complexity.

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

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

Schema coverage is 100%, so parameters are already described. Description mentions 'Top N edges' aligning with schema, but adds no extra semantics beyond defaults.

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 scans high-volume Polymarket markets and returns those where Pipeworx data disagrees. Specifies verb (scan/return), resource (markets), and distinguishes from sibling tools like polymarket_arbitrage.

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?

Description explicitly positions the tool as answering 'what should I bet on today' and discovering opportunities without manual paging. However, it lacks explicit when-not-to-use or comparison to 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?

Discloses that the tool retrieves from 'this session or previous sessions,' providing persistence context. With no annotations, the description adds value beyond the schema. Does not mention any side effects or limitations.

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

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

Single, well-structured sentence that front-loads the primary action. No redundant words; every part 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 a simple tool with no output schema and 1 parameter, the description fully explains the tool's functionality. Could mention return format or error handling, but not necessary for clarity.

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 repeats that omitting key lists all memories, which is already in the schema property description. Adds no new parameter-level detail.

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 memories, distinguishing between the two behaviors. The verb 'retrieve' is specific, and the resource 'memory' is defined. Differentiates from sibling tools like 'remember' and 'forget'.

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 it to retrieve context saved earlier, and notes the alternative behavior when key is omitted. Does not explicitly state when not to use it or mention alternatives, 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?

With no annotations, description fully discloses fan-out to three data sources in parallel, return structure (changes, count, URIs), and limitation to 'company' type. Does not mention any side effects, but as a read query this is acceptable transparency.

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

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

Four concise sentences, no fluff. Front-loaded with purpose, then details, ending with use case. 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?

No output schema, so description covers return format (structured changes, count, URIs). Covers entity type constraints, time formats, and behavior. Complete for a query tool with 3 parameters.

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?

Adds significant meaning beyond the input schema: explains fan-out behavior for type, provides examples for since format, details return values, and hints at valid value formats (ticker/CIK). Schema coverage is 100% but description enriches all 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?

Description explicitly states 'What's new about an entity since a given point in time', with specific verb ('what's new'), resource ('entity'), and time window. It distinguishes from sibling tools by detailing the multi-source fan-out behavior for company type.

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?

Clearly states use cases: 'brief me on what happened with X' or change-monitoring workflows. No explicit exclusions or alternatives, but the context is sufficient for an agent to infer 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?

Discloses memory persistence behavior (authenticated vs anonymous sessions) beyond what is known. No annotations are provided, so the description carries full burden; it effectively communicates the tool's behavioral traits 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?

Two concise sentences that front-load the core action and then provide usage guidance and behavioral 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?

Given the tool's simplicity (2 params, no output schema), the description is complete: it explains purpose, usage, and persistence. It could optionally mention key format constraints (e.g., case sensitivity) but is otherwise 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?

Input schema covers 100% of parameters with good descriptions and examples. The description adds value by explaining the purpose of storing key-value pairs, but does not add additional meaning beyond the schema's existing 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 verb 'Store' and resource 'key-value pair in your session memory', differentiating from siblings like 'recall' and 'forget' which are for retrieval and deletion respectively.

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 ('save intermediate findings, user preferences, or context across tool calls') and provides context on persistence differences between authenticated and anonymous users.

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 description fully discloses return values (ticker, CIK, name, URIs) and input constraints. Does not explicitly state read-only nature, but implies it via 'lookup' 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 sentences, front-loaded with purpose, no extraneous information. 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?

Fully describes inputs, outputs, and benefits despite no output schema. Complete for a straightforward lookup tool.

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

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

Schema coverage 100% with descriptions. Description adds examples (AAPL, 0000320193, Apple) and explains type enum 'company' is v1, enhancing schema meaning.

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 verb 'Resolve', resource 'entity', and outcome 'canonical IDs'. Distinguishes from siblings by noting it replaces multiple 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?

Explicitly specifies when to use (resolve entities), input formats (ticker, CIK, name), and efficiency benefit. Lacks explicit when-not-to-use or alternatives, but 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?

With no annotations provided, the description carries the full burden. It discloses the sources (SEC EDGAR + XBRL), the returned verdict types, and that it provides citations. It does not detail any potential side effects (likely none) or limitations like only supporting public US companies, but the description is reasonably transparent for a fact-checking 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 two sentences, front-loaded with the main purpose. The first sentence immediately states the core function. The second sentence provides details on the return value and scope. No redundant information, 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 tool has only one parameter, no output schema, and no annotations, the description covers the key aspects: purpose, domain, inputs, and outputs. It could benefit from mentioning error handling or limitations (e.g., only public US companies), but it is fairly complete for a simple tool.

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

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

The single parameter 'claim' has a good description in the schema (100% coverage). The tool description adds examples and clarifies the expected format ('Natural-language factual claim'). Beyond that, it does not add significant new meaning, so a baseline score of 3 is appropriate.

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

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

The description clearly states it fact-checks natural-language claims against authoritative sources, specifies the domain (company-financial claims for public US companies via SEC EDGAR + XBRL), and lists the return values (verdict, structured form, citation, delta). It also distinguishes itself from sibling tools by noting it replaces 4-6 sequential agent 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 clearly indicates when to use this tool: for fact-checking natural-language financial claims. It implicitly restricts to US public company financial data. It does not explicitly state when not to use it or compare to individual sibling tools, but the context is sufficient for an agent to decide.

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