Coinpaprika

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

With no annotations, description should disclose behavioral traits. It explains automatic tool selection and argument filling but omits error handling, latency, authentication, or failure modes. Adequate for a search tool but lacks full 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?

Description is concise (around 100 words), well-structured: purpose, usage, capabilities, examples. No fluff, front-loaded.

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

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

Given the tool's complexity (one parameter, no output schema, no annotations), the description is fairly complete. It covers what, when, and how with examples. Could include output format but overall 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?

Single parameter 'question' has schema description covering usage. Description adds value with examples but does not significantly extend understanding beyond the schema. Baseline 3 due to 100% schema coverage.

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

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

Description clearly states the tool answers natural-language questions by routing to the appropriate data source. It distinguishes itself from sibling tools like get_coin or entity_profile by being a general-purpose query interface.

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

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

Explicitly tells when to use: when user asks open-ended questions like 'What is X?' and doesn't want to pick the specific tool. Provides examples. Does not explicitly state when not to use, but context implies alternatives are better for known specific queries.

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 discloses the data sources (SEC EDGAR/XBRL, FAERS) and the metrics retrieved (revenue, net income, cash, debt for companies; adverse events, approvals, trials for drugs). It mentions return format (paired data + citation URIs). While it could mention potential latency from external API calls, it is transparent about its core 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?

The description is two sentences long, efficiently conveying the purpose and detailed use cases. The first sentence front-loads the main action, and the second provides essential context without unnecessary words. Slightly dense but 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?

Covers the main functionality and data sources but lacks details on error handling (e.g., invalid ticker/drug name), return structure beyond 'paired data', and any rate limits. Given the tool's complexity (2 entity types, multiple metrics), these gaps are notable but not critical.

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

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

Schema coverage is 100%, so the schema already documents both parameters. The description adds value by explaining the format of 'values' (tickers/CIKs vs drug names) and the allowed range (2-5). It also clarifies the meaning of the 'type' enum beyond the schema's brief description.

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 'compare', the resource 'companies or drugs', and the number range '2-5'. It distinguishes from siblings by noting it replaces 8-15 sequential calls, making its unique value explicit.

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?

Provides explicit examples of when to use the tool ('compare X and Y', 'vs', 'stack up', etc.) and separates use cases by entity type (company vs drug). However, it lacks explicit 'when not to use' guidance, though the context is sufficient for an agent.

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

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

No annotations are provided, so the description carries the full burden. It implies read-only discovery but does not explicitly state that it is non-destructive or safe. The description could be more transparent about side effects or rate limits, but the intent is clear.

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 somewhat long but front-loaded with the core purpose. Every sentence adds value, listing example domains and usage guidance. It could be slightly more concise, but it is not verbose.

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, no output schema, and 100% schema coverage, the description adequately covers purpose, usage, and parameters. It mentions the return format (top-N relevant tools with names and descriptions), which is sufficient.

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

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

Input schema coverage is 100%, so the schema already documents both parameters. The description adds a natural language description for the query parameter, but this does not add new semantics beyond the schema. The limit parameter is also fully documented in 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 tool finds tools by describing a data/task, lists many example domains, and distinguishes itself as a discovery tool to call first, which differentiates it from sibling tools that perform specific 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 explicitly says 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' It also gives examples of when to use, providing clear guidance on when this tool is appropriate.

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

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

No annotations are provided, so the description carries the full burden. It discloses the input format and return data, but does not mention error handling, rate limits, or internal aggregation behavior. It is fairly transparent for a read tool but leaves gaps.

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 with front-loaded purpose and usage examples. It is comprehensive but somewhat dense. Every sentence adds value, but could be restructured for clarity.

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 compensates by listing return types. However, it lacks details on failure modes, pagination, or size limits, leaving some gaps for a tool returning multiple data sources.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds value by clarifying the 'type' enum constraint and explicitly stating names are not supported for 'value', directing to resolve_entity.

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

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

The description clearly states the tool's purpose: 'Get everything about a company in one call.' It enumerates specific data returned (SEC filings, fundamentals, patents, news, LEI) and provides example queries, distinguishing it from needing multiple tools.

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

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

The description explicitly lists use cases: when a user asks for company information. It advises using resolve_entity if only a name is available. It implicitly distinguishes from compare_entities and search, but lacks explicit when-not-to-use for other scenarios.

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 correctly implies deletion (destructive) but doesn't elaborate on edge cases like non-existent keys or side effects. Still adequate for a simple 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 concise sentences with clear purpose, usage guidance, and tool relations. 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 simple tool with one parameter and no output schema, the description covers purpose, usage, and context completely.

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, meeting the baseline for high coverage.

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

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

The description clearly states 'Delete a previously stored memory by key', with a specific verb (delete) and resource (memory). It distinguishes from siblings by naming 'remember' and 'recall' as complementary 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 lists when to use: 'when context is stale, the task is done, or you want to clear sensitive data'. Provides clear context with no ambiguity.

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 fully disclose behavioral traits. It only mentions the contents of the profile but fails to describe whether the operation is read-only, any rate limits, or error handling for invalid coin IDs. The behavior is implied but not explicit.

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 is appropriately front-loaded. However, it could be slightly more informative without being verbose, such as mentioning that it returns data for a single coin.

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

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

The tool has no output schema, so the description should explain the return value in more detail. It lists some fields but says 'Full coin profile' without specifying all possible fields or structure. Given the simplicity of the tool, it is minimally acceptable but not complete.

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

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

The input schema provides 100% description coverage for the single parameter 'coin_id' with a clear example. The tool's description does not add additional meaning beyond what the schema offers, 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 that this tool returns a full coin profile including description, team, links, and tags. It uses a specific verb (get) and resource (coin profile), and it distinguishes itself from sibling tools like list_coins and tickers_latest which serve different purposes.

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 does not provide any guidance on when to use this tool versus alternatives, such as when to prefer get_coin over list_coins or entity_profile. There is no mention of prerequisites or context for 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?

Annotations are absent. The description does not disclose update frequency, caching behavior, rate limits, or whether the data is real-time. For a simple data retrieval tool, minimal behavioral context is expected but 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 sentence, highly concise. It front-loads the key information. However, it could be structured with a clearer verb and bullet points for readability.

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 is the sole documentation. It lists four metrics but lacks details on data sources, update cadence, or format (e.g., currency units). For a simple tool this is minimally adequate.

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?

There are zero parameters, so the baseline is 4. The description adds meaning by listing the specific metrics returned, which is necessary since the input schema is empty. It adequately compensates for the lack of schema parameters.

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

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

The description clearly states the output: total market cap, 24h volume, BTC dominance, and active currencies. It uses specific nouns for the data returned, making the tool's purpose evident. However, it does not explicitly state that this is a read-only lookup, and sibling tools like 'get_coin' or 'tickers_latest' could overlap in context.

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 versus alternatives such as 'get_coin' or 'tickers_latest'. There is no mention of use cases, prerequisites, or limitations.

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?

Adds behavioral context by disclosing the free tier restriction, which impacts usage assumptions. However, with no annotations, it fails to mention other behaviors like rate limits, authentication needs, or effects of requesting beyond free tier.

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?

Extremely concise single sentence with no wasted words. Front-loads core data and constraint effectively.

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?

Lists main output fields (OHLC, volume, market cap), but with no output schema, lacks detail on format or response structure. For a 5-parameter tool, description is sparse and doesn't cover edge cases or required parameter 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 already documents 4 of 5 parameters (80% coverage). Description adds slight context about the time range limitation but does not elaborate on any specific parameter beyond what schema provides. The required coin_id parameter remains undocumented in schema, and description offers no help.

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 provides daily OHLC, volume, and market cap, distinguishing it from other tools that might offer different data like current prices or coin metadata. However, it does not explicitly differentiate from siblings like tickers_latest or global_market.

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?

Only mentions free tier limit (up to 1 year back), but provides no guidance on when to use this tool vs alternatives, or when not to use it. No mention of prerequisites or use cases.

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

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

No annotations provided, so description carries full burden. Does not disclose potential behavioral traits like large payload size, pagination, or read-only nature. Only states output fields.

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, front-loaded with key information. 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?

Adequately describes output for a parameterless list tool. Lacks mention of ordering or pagination but sufficient for simple 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?

No parameters exist, so description adds no parameter-level meaning. Baseline is 3 due to trivial 100% schema description coverage.

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

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

Clearly states it lists all tracked coins (~25k) and specifies the fields returned. Distinct from sibling tools like get_coin and search.

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?

Implies usage for getting a full list but does not explicitly state when to use versus alternatives like get_coin or search. No exclusion criteria.

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

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

With no annotations, the description fully discloses behavioral traits: rate-limited to 5 per identifier per day, free, doesn't count against tool-call quota, and mentions the team reads digests daily affecting roadmap. 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?

The description is a single paragraph of about 5-6 sentences, each adding essential information. It is structured logically: purpose, usage scenarios, behavioral notes, and constraints. No wasted words, but slightly longer than minimal.

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 covers when to use, how to provide feedback, constraints (rate limit, quota), and what not to do. It is fully adequate for an AI agent to invoke the tool correctly without needing additional context.

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?

All parameters have schema descriptions (100% coverage), but the description adds extra context: explains the 'type' enum values in a user-friendly way, provides guidelines on specificity and length (2000 chars max), and instructs not to paste user prompts. Adds meaningful value beyond schema.

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

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

The description clearly states the tool's purpose: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It specifies the types of feedback (bug, feature, data_gap, praise), distinguishing it from sibling tools that are data retrieval or analysis 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 provides when-to-use criteria for each feedback type ('Use when a tool returns wrong/stale data...', 'when a tool you wish existed...', 'when something worked surprisingly well...'), and includes a negative guideline: 'don't paste the end-user's prompt.' Also notes rate limit and quota exemption.

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

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

No annotations are provided, so the description carries the full burden. It discloses the tool is a read-only lookup (non-destructive), scoped by identifier, and can list all keys if omitted. It does not detail error behavior or rate limits, but these are less critical for a retrieval tool.

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

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

Two sentences with no wasted words. The main action and key details 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's simplicity (one optional parameter, no output schema), the description covers purpose, usage, scoping, and relation to siblings. It is complete for an agent to understand when and how to invoke.

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 one optional parameter described as 'Memory key to retrieve (omit to list all keys)'. The description adds value by explaining the key omission behaviour ('list all saved keys') and context on usage, going beyond schema.

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

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

The description clearly states the tool retrieves a saved value or lists all keys, distinguishing it from sibling tools like remember and forget. It specifies the verb 'Retrieve' and the resource 'value previously saved via remember', with scope and behavior.

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 'Use to look up context the agent stored earlier' and provides examples, giving clear context. It also notes scoping and pairing with remember/forget, but lacks explicit when-not-to-use or exclusions.

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

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

Given no annotations, the description fully covers behavior: it discloses parallel fan-out to three sources, the return format (structured changes, count, URIs), and parameter format options. Could be improved by mentioning potential timeouts or data freshness, but overall 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 concise with no superfluous words. It is front-loaded with the core question, followed by usage examples, data sources, parameter guidance, and return structure. Every sentence serves a purpose.

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 covers purpose, usage, parameters, and return format adequately. With no output schema, it describes what is returned. It lacks mention of error handling or empty results, but overall provides sufficient context for an agent to use the tool effectively.

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

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

Schema coverage is 100%, but the description adds significant value by providing concrete examples for 'since' (e.g., '7d', '30d', '1y') and clarifying that 'value' accepts ticker or CIK, and that 'type' only supports company. This goes beyond the generic schema descriptions.

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

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

The description opens with a clear question 'What's new with a company in the last N days/months?' and provides specific user query examples. It explicitly states the tool fans out to multiple data sources (SEC EDGAR, GDELT, USPTO) and is distinct from siblings like search or entity_profile.

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?

Usage is well-defined with concrete query examples ('what's happening with X?', 'news on Apple this month'). The description explains when to use it for monitoring changes. However, it does not explicitly mention when NOT to use it or compare to alternative 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 provided, so description carries the full burden. Describes key-value storage, scoping by identifier, persistence duration (24h for anonymous, persistent for authenticated). Does not mention whether duplicate key overwrites or if there are size limits, but covering the key behavioral traits.

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

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

Very concise: two sentences cover purpose, usage, storage details, and persistence. Front-loaded with critical information. No wasted words.

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

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

For a simple key-value store with 2 parameters and no output schema, the description covers purpose, usage, persistence, and relation to siblings. Missing details on overwrite behavior or storage limits, but sufficient for typical use.

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

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

Schema coverage is 100% with descriptive parameter descriptions. The description adds context about key being a memory key and value being any text, but the schema already provides similar examples. Baseline 3 is appropriate as description adds marginal value.

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 saves data for reuse across conversations or sessions, providing examples like resolved ticker, user preference. It distinguishes itself from siblings recall and forget by naming them.

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

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

Explicitly states when to use (discover something worth carrying forward) and mentions paired tools recall and forget. Also notes persistence differences for authenticated vs 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?

With no annotations, description discloses that tool performs a lookup (non-destructive), returns IDs and citation URIs, and replaces multiple calls. Sufficiently transparent for a read-only lookup; could mention rate limits or exact response shape but acceptable.

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

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

Four sentences, efficiently front-loaded with core purpose, followed by examples and usage guidance. No redundancy; 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?

Tool is simple (two parameters) and description covers input, output format (IDs + URIs), and order of use. Lacks output schema but compensates by describing return values. Minor gaps like error handling are not critical for 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?

Schema covers 100% of parameters with descriptions. Description adds examples (Apple→AAPL, Ozempic→RxCUI) and clarifies acceptable input formats (ticker, CIK, name), exceeding baseline by reducing ambiguity.

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

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

Description uses specific verb ('Look up') and resource ('canonical/official identifier for a company or drug'), clearly distinguishing from sibling tools like entity_profile or search by specifying the ID types (CIK, ticker, RxCUI, LEI). Examples solidify 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?

Explicitly states when to use ('when a user mentions a name and you need the CIK...') and recommends using before other tools. Does not explicitly list alternatives but context implies it's for identifier resolution, not general search or comparison.

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 disclose all behavioral traits. It only mentions 'fuzzy search' but omits details like whether search is case-sensitive, how results are ranked, what fields are searched (name, description, ticker?), and whether there are side effects or access restrictions.

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, making it concise but overly terse. It front-loads the purpose but lacks structure such as separate lines for when to use or parameter hints. Every word is used, but more detail would improve clarity.

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 is insufficient. It does not explain what the tool returns (e.g., list of items with type and name), how results are ordered, or whether queries are prefix-based. For a general search tool, this leaves significant gaps 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 description coverage is 67%, with two parameters (limit, modifier) having descriptions. The tool description adds no extra meaning beyond the schema, such as explaining that 'query' can be a partial name or that 'modifier' restricts search to ticker symbols. 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 performs fuzzy search across five distinct categories (coins, exchanges, ICOs, people, tags). This distinguishes it from sibling tools like get_coin (single entity) or list_coins (list all), as it is a cross-category search.

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 versus alternatives (e.g., entity_profile for specific entities, list_coins for simple listing). There is no mention of query syntax, result ordering, or when to prefer this search over other tools.

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

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

With no annotations, the description must fully convey behavior. It states the output includes price, market cap, volume, and explains the conditional behavior of coin_id. However, it omits details like pagination for bulk results, data freshness, or any error states, leaving some transparency gaps.

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 packs essential information: output fields and parameter-dependent behavior. No wasted words; every part 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 no output schema, the description should detail return format. It mentions fields (price, market cap, volume) but not types or structure. Complexity is low, so the description is mostly adequate but could be more specific about the response shape for all vs. single ticker.

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 50% (quotes described, coin_id not). The description adds meaning for coin_id by stating its effect on output cardinality. For quotes, the description does not add beyond the schema's mention of comma-sep currencies and defaults, but the added context for coin_id compensates, giving above-baseline value.

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

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

The description clearly states the tool returns 'Latest price + market cap + volume' and distinguishes two modes based on coin_id parameter. It specifies the resource (tickers/latest data) and uses specific verbs, differentiating it from siblings like 'get_coin' and 'list_coins'.

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 guides when to use the coin_id parameter vs without: 'Without coin_id returns all tickers; with coin_id returns one.' This provides clear context for the two use cases, though it does not directly compare to sibling tools or mention when not to use the 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?

No annotations provided, so description carries full burden. Discloses return format (verdict types, citation, percent delta) and limitations (v1 only for company-financial claims). Lacks mention of error conditions or performance, but sufficient for typical usage.

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 that each serve a distinct purpose: definition, usage guidance, and scope/returns. No redundant or extraneous information. Efficient and well-organized.

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 single-parameter tool with no output schema, the description covers purpose, usage, supported domain, and return structure. It even notes what tool it replaces. Comprehensive enough for an agent to use correctly.

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

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

Schema coverage is 100%, baseline 3. Description adds meaning by specifying the domain (company-financial claims via SEC EDGAR) and providing example claims, which helps the agent form correct inputs beyond the schema's type description.

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?

Uses specific verb+resource (fact-check/verify claim) and clearly distinguishes from siblings by specializing in company-financial claims via SEC EDGAR. Examples and usage context make purpose unambiguous.

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

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

Explicitly states when to use (checking truth of user statements) and gives query examples. Does not mention when not to use or provide alternative tools, but the scope is clearly delineated.

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