usgswater

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: it's a query tool that interprets natural language, selects data sources automatically, and returns results. However, it lacks details on limitations (e.g., rate limits, error handling, or specific data source constraints), which prevents a perfect score.

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 front-loaded with the core functionality, followed by supportive details and examples. Every sentence earns its place by clarifying usage, differentiating from alternatives, or providing concrete examples, with no redundant or vague language.

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 (natural language processing with automatic tool selection) and no output schema, the description is mostly complete. It covers purpose, usage, and parameter semantics well, but lacks information on output format or potential errors, which could aid the agent in handling responses.

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

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

The schema description coverage is 100%, so the baseline is 3. The description adds value by emphasizing the parameter's nature ('question or request in natural language') and providing examples that illustrate expected input formats, enhancing understanding beyond the schema's basic type definition.

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: 'Ask a question in plain English and get an answer from the best available data source.' It specifies the verb ('ask'), resource ('answer'), and mechanism ('Pipeworx picks the right tool, fills the arguments'). It distinguishes from siblings by emphasizing natural language input versus tool-specific schemas.

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

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

The description explicitly states when to use this tool: 'No need to browse tools or learn schemas — just describe what you need.' It provides clear alternatives (implicitly, use other tools for structured queries) and includes three concrete examples to illustrate appropriate use cases, making it highly actionable for an AI 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?

Annotations already declare read-only and open-world. Description adds details: classification, fan-out to packs, return of evidence packet and comparison. 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 four sentences, front-loaded with purpose. Some redundancy (mentions 'core demo product'), but overall each 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, but description explains return type (evidence packet, comparison). Mentions fan-out and classification. Adequate for a complex tool with high schema coverage.

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 both parameters (100% coverage). Description adds meaning: explains 'market' can be slug, URL, or question text; 'depth' enum values are elaborated ('quick = 2-3 sources, thorough = full fan-out').

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 researches a Polymarket bet using Pipeworx data. It specifies inputs (slug, URL, or question text) and outputs (evidence packet, comparison). Differentiates from siblings by being a composite 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 use cases are provided: 'should I bet on X?', 'what does the data say?', 'is there edge?'. No explicit when-not-to-use or 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?

No annotations provided; description details data sources (SEC EDGAR, FDA) and output (paired data + URIs). It doesn't mention auth or side effects, but the read-only nature is inferred. Still, it provides good behavioral context beyond bare 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?

Concise three-sentence structure with front-loaded purpose, bullet-like type details, and efficiency note. No wasted words, easily scannable for an AI agent.

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

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

Given no output schema, description explains return values (paired data + URIs) and type-specific fields. It's self-contained, covering inputs, outputs, and use-case efficiency.

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 has 100% coverage, but description adds meaningful type-specific details (e.g., revenue, trial count) and clarifies array formatting. This exceeds baseline by enriching parameter understanding.

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

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

Description clearly states the tool compares 2-5 entities side-by-side, specifying data fields per type. It distinguishes itself from sibling tools (e.g., ask_pipeworx, get_daily) which handle single entities or different operations.

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 use for efficient comparison (replaces 8-15 sequential calls) and specifies entity types. However, no explicit when-not-to-use or alternatives are given, though siblings are not direct 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?

With no annotations provided, the description carries the full burden and does well by explaining the search behavior ('Returns the most relevant tools with names and descriptions') and the 'FIRST' call recommendation. It doesn't detail rate limits, authentication needs, or error handling, but covers core functionality adequately.

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 front-loaded with the main purpose, followed by usage guidance in two sentences. Every sentence adds value without redundancy, making it efficient and well-structured for quick 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 the tool's moderate complexity (search function with 2 parameters), no annotations, and no output schema, the description is mostly complete. It explains what the tool does and when to use it, but lacks details on output format or error cases, which could be helpful for an agent.

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

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

Schema description coverage is 100%, so the schema already documents both parameters ('query' and 'limit'). The description adds no additional parameter semantics beyond what the schema provides, such as examples or constraints, 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 the tool's purpose with specific verbs ('Search the Pipeworx tool catalog') and resource ('tool catalog'), distinguishing it from sibling tools like 'get_current', 'get_daily', and 'search_sites' by focusing on tool discovery rather than data retrieval or site searching.

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

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

The description provides explicit usage guidelines: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This gives clear context on when to use it (large catalog, initial search) and implies alternatives are not needed first in such 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?

Describes return format (pipeworx:// citation URIs) and efficiency gains (replaces 10-15 calls). No annotations exist, so the description carries full burden; missing details like rate limits or data freshness but sufficient for safe invocation.

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 dense, front-loaded sentences with zero waste. Every clause adds critical information (scope, content, limitations, alternatives).

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 high complexity, the description adequately covers what data is returned (lists categories) and how to use it (citation URIs). Also addresses edge cases (federal contracts) and pre-condition (name resolution).

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 value beyond the input schema: explains type constraint (only company), clarifies value formats (ticker vs CIK), and warns about name support, all of which are not in the schema descriptions.

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

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

The description clearly states it returns a full entity profile across multiple Pipeworx packs, listing specific data types (SEC filings, XBRL, patents, news, LEI) and explicitly distinguishes from the sibling tool usa_recipient_profile for federal contracts.

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 when-to-use (comprehensive company profile) and when-not-to-use (federal contracts -> usa_recipient_profile; name resolution -> resolve_entity). No ambiguities.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool deletes a memory, implying a destructive mutation, but lacks details on permissions, reversibility, error handling, or side effects. This is inadequate for a mutation tool with zero annotation coverage.

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, efficient sentence with zero waste. It is front-loaded with the core action ('Delete') and resource ('stored memory'), making it easy to parse and understand quickly without unnecessary elaboration.

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 destructive mutation tool with no annotations and no output schema, the description is incomplete. It fails to address critical aspects like what happens after deletion (e.g., confirmation, error messages), whether the operation is permanent, or any dependencies, leaving significant gaps for agent usage.

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

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

Schema description coverage is 100%, with the parameter 'key' documented as 'Memory key to delete'. The description adds minimal value beyond this, only reinforcing the parameter's purpose without providing additional context like key format or examples. Baseline 3 is appropriate when the schema does the heavy lifting.

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 specific action ('Delete') and resource ('a stored memory by key'), distinguishing it from sibling tools like 'recall' (retrieve) or 'remember' (store). It uses precise language that directly communicates the tool's function without 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 provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites (e.g., needing an existing memory key), exclusions, or comparisons to siblings like 'recall' (for retrieval) or 'remember' (for storage), leaving the agent to infer usage 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?

No annotations are provided, so the description carries full burden. It states what data is retrieved but doesn't disclose behavioral traits like rate limits, authentication needs, response format, error conditions, or whether this is a read-only operation. The description doesn't contradict annotations (none exist), but it lacks essential operational context for a tool with no annotation coverage.

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, efficient sentence that front-loads the core functionality ('Get current instantaneous streamflow...'). Every word earns its place by specifying data types, units, and scope without redundancy. It's appropriately sized for a simple tool with one parameter.

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

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

Given the tool's low complexity (1 parameter, no nested objects) and 100% schema coverage, the description is minimally adequate. However, with no annotations and no output schema, it should ideally provide more behavioral context (e.g., response structure, error handling). The description covers the basic purpose but leaves operational details unspecified, which could hinder an agent's ability to use it correctly without trial and error.

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%, with the single parameter 'site_id' well-documented in the schema. The description doesn't add any parameter-specific information beyond what's in the schema, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but also doesn't need to given the schema's completeness.

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 with specific verbs ('Get current instantaneous') and resources ('streamflow (discharge, cfs) and gage height (ft) for a USGS monitoring site'). It distinguishes from sibling tools by specifying 'current instantaneous' data rather than daily aggregates (get_daily) or site searches (search_sites). However, it doesn't explicitly contrast with siblings in the text.

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 context by specifying 'current instantaneous' data, suggesting this tool is for real-time measurements rather than historical or search functions. However, it doesn't provide explicit guidance on when to use this versus alternatives like get_daily for daily averages or search_sites for finding sites. No exclusions or prerequisites 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 the date format requirement (YYYY-MM-DD) which is useful behavioral context, but doesn't mention other important traits like rate limits, authentication needs, error handling, or what happens with invalid dates/sites. The description doesn't contradict any annotations (none exist).

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 are front-loaded with the core purpose followed by an important constraint. Every word earns its place with no redundant information or unnecessary elaboration.

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 read-only data retrieval tool with no annotations and no output schema, the description provides adequate but minimal context. It covers the purpose and date format requirement, but doesn't explain what the return values look like (e.g., data structure, units) or address potential limitations. Given the tool's relative simplicity, this is minimally viable but has clear gaps.

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

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

Schema description coverage is 100%, so the schema already fully documents all three parameters. The description adds the date format constraint (YYYY-MM-DD) which provides additional semantic context beyond the schema's parameter descriptions. This meets the baseline expectation when schema coverage is high.

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 specific action ('Get daily mean streamflow values'), resource ('for a USGS site'), and scope ('over a date range'). It distinguishes from sibling tools like 'get_current' (which likely provides current data) and 'search_sites' (which searches for sites rather than retrieving 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?

The description implies usage for retrieving historical daily mean streamflow data, but doesn't explicitly state when to use this tool versus alternatives like 'get_current' (for real-time data) or 'search_sites' (for finding sites). No explicit when-not-to-use guidance or prerequisites are provided.

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 full behavioral burden. It reveals the tool is non-destructive (sends feedback) and mentions a rate limit of 5 messages per identifier per day. However, it does not describe authentication requirements, response format, or any side effects beyond sending.

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

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

The description is three sentences long, front-loaded with the purpose, then use cases, then constraints. Every sentence earns its place without redundancy.

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

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

Given the tool's simplicity (no output schema, three parameters, straightforward submission), the description covers all necessary aspects: purpose, use cases, content guidelines, and rate limiting. It is complete for an agent to decide and invoke correctly.

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

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

Schema description coverage is 100%, meaning the input schema already fully documents each parameter. The description adds value by providing usage context (e.g., rate limit, content rules) but does not elaborate on parameter meanings beyond what the schema provides.

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

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

The description clearly states the tool sends feedback to the Pipeworx team and lists specific use cases: bug reports, feature requests, missing data, or praise. This distinguishes it from sibling tools, which are primarily data retrieval or interaction 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 states when to use (for bugs, features, data gaps, praise) and includes important guidelines: do not include the end-user's prompt verbatim, and mentions a rate limit. It does not explicitly contrast with alternatives, but siblings are dissimilar.

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 explains the algorithm (walking child markets, extracting dates/thresholds, sorting, reporting violations) beyond the annotations' safety profile, though it omits details like rate limits or permissions.

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 informative paragraph, front-loaded with purpose, but could be more structured (e.g., bullet points). Every sentence adds value without redundancy.

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

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

Given the single parameter and no output schema, the description fully explains the input, internal logic, and return format, making it complete for the tool's purpose.

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

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

The parameter 'event' is described in the schema as a slug or URL, and the description adds extra context with an example and explanation of how it is used (to walk child markets), enhancing understanding beyond the schema.

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

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

The description clearly states the tool finds arbitrage opportunities via monotonicity violations in Polymarket events, distinguishing it from siblings by its specific domain and logic.

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 when analyzing Polymarket events for arbitrage but does not explicitly state when not to use it or suggest alternative tools like polymarket_edges or bet_research.

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 readOnlyHint=true and destructiveHint=false. The description adds significant behavioral detail: it groups by asset, fetches each asset's price history ONCE, computes model probability per market, ranks by |edge|, and returns top N with suggested trade direction. No contradictions with annotations.

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

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

The description is a single paragraph that front-loads the main purpose. While it contains all necessary detail, it is somewhat lengthy. It could be more concise without losing clarity, but it does not contain filler.

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 having no output schema, the description fully explains what is returned (top N ranked by edge magnitude with suggested trade direction) and how it works (model, grouping, caching). It addresses the core question and covers all aspects needed for an agent to decide to invoke it.

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

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

Input schema has 100% description coverage for all three parameters (limit, window, min_edge_pp). The description repeats some defaults (e.g., 'Default 10, max 25' for limit) but adds no new semantic value beyond what the schema already provides. Baseline 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 the tool scans high-volume Polymarket markets and returns those where Pipeworx data disagrees most with market price. It is specific about the use case: 'Built for the “what should I bet on today” question'. This distinguishes it from siblings like polymarket_arbitrage, which likely focuses on arbitrage, not opportunity discovery via edge.

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: to discover opportunities without manual paging. It mentions it's V1 covering crypto-price bets, implying limitations. However, it does not explicitly exclude other uses or contrast with sibling tools like ask_pipeworx or bet_research, so usage is clear but not exhaustive.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adequately describes the core behavior (retrieval vs listing based on parameter presence) and mentions persistence across sessions. However, it doesn't address potential limitations like memory size constraints, retrieval failures, or authentication requirements that might be relevant for a memory system.

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 perfectly concise with two sentences that each earn their place. The first sentence states the dual functionality, and the second provides usage context. There is zero wasted text, and the information is front-loaded with the core purpose immediately clear.

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 retrieval tool with 1 optional parameter and 100% schema coverage, the description provides good context about persistence across sessions and the dual retrieve/list behavior. However, without an output schema, it doesn't describe what format memories are returned in (e.g., structured data, text) or potential error conditions, leaving some gaps in completeness.

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

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

The schema has 100% description coverage, so the baseline is 3. The description adds valuable semantic context by explaining the dual functionality: retrieving by key versus listing all when key is omitted. This clarifies the optional parameter's significance beyond what the schema's technical description provides, justifying a score above baseline.

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 with specific verbs ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'). It distinguishes this tool from siblings like 'remember' (which likely stores memories) and 'forget' (which likely deletes them) by focusing on retrieval operations.

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

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

The description provides explicit guidance on when to use this tool: 'to retrieve context you saved earlier in the session or in previous sessions.' It also specifies when to omit the key parameter ('omit key to list all keys'), giving clear operational instructions. This distinguishes it from alternatives like 'get_current' or 'get_daily' which likely serve different retrieval purposes.

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 parallel fan-out to three sources, return structure (structured changes, count, pipeworx:// URIs), and date format options. Lacks explicit read-only statement, but overall provides useful behavioral context beyond annotations (none provided).

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

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

Concise yet complete: purpose, behavior, parameter details, return format, usage recommendations—every sentence earns its place. Well-structured for quick scanning.

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 parameter usage, behavior, and return format despite no output schema. Missing error handling or concurrency details, but sufficient for a query tool with moderate 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 covers all parameters (100%). Description adds value for 'since' with format examples and monitoring recommendation, though repeats schema for 'type' and 'value'. Extra guidance elevates it above baseline 3.

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

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

The description clearly states 'What's new about an entity since a given point in time', specifying verb ('what's new'), resource ('entity'), and time window. It details the fan-out for type=company to SEC, GDELT, USPTO, making it distinct from siblings like entity_profile or compare_entities.

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

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

Explicitly recommends use for 'brief me on what happened with X' or change-monitoring workflows. While it doesn't list alternatives or exclusions, the context is clear and differentiates from static profile 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 provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: the tool performs a write operation ('store'), specifies persistence behavior ('authenticated users get persistent memory; anonymous sessions last 24 hours'), and hints at session-scoped storage. However, it lacks details on error handling, size limits, or concurrency implications.

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 front-loaded with the core purpose in the first sentence, followed by usage context and behavioral details. Every sentence adds value without redundancy, and the structure is efficient with no wasted words, making it easy to parse quickly.

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

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

Given the tool's moderate complexity (write operation with persistence nuances), no annotations, and no output schema, the description does a good job covering purpose, usage, and key behaviors. However, it lacks information on return values or error cases, which would be helpful for an agent invoking the tool. It compensates well but isn't fully 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 description coverage is 100%, so the schema already documents both parameters ('key' and 'value') thoroughly. The description does not add significant meaning beyond the schema, such as explaining parameter interactions or constraints. The baseline of 3 is appropriate since the schema handles most of the parameter documentation.

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 with specific verbs ('store a key-value pair') and resources ('in your session memory'), distinguishing it from siblings like 'recall' (retrieval) and 'forget' (deletion). It explicitly mentions what gets stored ('intermediate findings, user preferences, or context across tool calls'), making the purpose unambiguous.

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

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

The description provides clear context on when to use this tool ('to save intermediate findings, user preferences, or context across tool calls'), but does not explicitly mention when not to use it or name alternatives. It implies usage for persistence needs without detailing scenarios where other tools like 'recall' might be more 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, so description carries full burden. Discloses it is a single call, returns specific fields, and mentions v1 supports only company type. Provides useful behavioral context like stable citation URIs.

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 with four sentences, front-loaded with the core purpose. No extraneous information, but could be slightly more 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?

With no output schema, the description adequately explains return values (ticker, CIK, name, URIs). Parameter coverage is complete. Lacks error handling or missing entity behavior, but overall sufficient 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?

Schema description coverage is 100%, so baseline is 3. The description adds value by giving examples for the 'value' parameter (e.g., AAPL, CIK, name) and clarifying the 'type' enum.

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 resolves entities to canonical IDs, specifies the verb 'resolve' and the resource 'entity', and differentiates 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?

Provides examples of input formats (ticker, CIK, name) and indicates when to use (single call to replace 2-3 lookup calls), but does not explicitly state when not to use or list 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'active' sites and 'real-time instantaneous data,' which hints at read-only behavior, but doesn't clarify permissions, rate limits, or response format. This leaves significant gaps for a tool that likely queries a database.

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

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

The description is a single, well-structured sentence that efficiently conveys the tool's purpose without unnecessary words. It is front-loaded with key information, making it easy for an agent to parse quickly.

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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., list of sites, error handling) or behavioral aspects like data freshness or limitations, which are crucial for a search tool with real-time data.

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% description coverage, fully documenting the 'state' parameter. The description adds no additional parameter semantics beyond what the schema provides, such as format details or constraints, so it meets the baseline score for high 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?

The description clearly states the tool's purpose with specific verbs ('Find active USGS stream-gage sites') and resources ('in a US state that have real-time instantaneous data'). It distinguishes the type of sites (active with real-time data) but doesn't explicitly differentiate from sibling tools like 'get_current' or 'get_daily', which prevents a perfect score.

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

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

The description provides no guidance on when to use this tool versus the sibling tools ('get_current' and 'get_daily'). It implies usage for finding sites with real-time data but lacks explicit when/when-not instructions or alternative recommendations, leaving the agent to infer 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?

No annotations provided, so description carries full burden. Describes the output (verdict types, extracted structured form, actual value with citation, percent delta) and the source (SEC EDGAR + XBRL). Discloses the v1 limitation to company-financial claims. Does not mention potential side effects or error handling, but the presence of 'inconclusive/unsupported' verdicts covers some failure modes.

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

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

Description is only 4 sentences, front-loading the core purpose. Each sentence adds distinct information: purpose, domain, output, and efficiency value. No redundant or unnecessary text.

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 sufficiently explains return values (verdict types, structured form, citation, delta). Covers input scope and limitations. Lacks explicit error or edge case handling, but verdict options implicitly cover unsupported cases. Overall adequate for an agent to 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 single required parameter 'claim' has a concise description in the schema with examples. The tool description adds value by clarifying the acceptable domains (company-financial claims) and the types of facts (revenue, net income, cash). Since schema description coverage is 100%, the extra context elevates it above baseline.

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 fact-checks natural-language claims against authoritative sources, specifying the supported domain (company-financial claims via SEC EDGAR + XBRL). The verb 'fact-check' with resource 'claim' is precise. Implicitly differentiates from siblings like ask_pipeworx by describing its specialized role and replacing multiple 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?

Explicitly describes the tool's usage context: for fact-checking company-financial claims of US public companies. Indicates it replaces 4-6 sequential agent calls, guiding when to use it for efficiency. Does not explicitly state when not to use, but the domain limitation is clear from the v1 scope.

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