Reddit

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

The description discloses that the tool selects the best data source, fills arguments, and returns results, which goes beyond the input schema. Since no annotations are provided, the description carries the full burden, and it adequately describes the tool's 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 concise, using only three sentences to convey purpose, usage, and examples. Every sentence adds value, and it is front-loaded with the core function.

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

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

Given the tool has a single parameter, no output schema, and no annotations, the description is fairly complete. It explains the orchestration behavior and provides examples, but lacks details on error handling or return format.

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

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

Schema coverage is 100%, and the description adds context about the parameter being a natural language question, which is already in the schema's description. The baseline is 3, and the description does not add much 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 takes a plain English question and returns an answer from the best available data source. It distinguishes itself from siblings by acting as an orchestrator that picks the right tool, but it doesn't explicitly name a sibling or differentiate from 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?

The description provides explicit examples of when to use this tool (e.g., asking about trade deficits, adverse events, SEC filings) and implies it's for natural language queries. However, it does not explicitly state when not to use it or mention alternatives.

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

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

Discloses steps: resolve market, classify bet, fan out to packs, return evidence packet and market-vs-model comparison. Consistent with annotations (readOnlyHint, openWorldHint, destructiveHint false), no contradiction.

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 packed with essential information, front-loaded purpose, 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?

Explains output (evidence packet, market-vs-model comparison) and behavior (fan-out based on bet type) despite no output schema; sufficient for understanding input and output.

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 baseline 3. Description adds examples for 'market' and restates enum values for 'depth' already in schema, minimal added 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 researches Polymarket bets using Pipeworx data, specifies the action, and distinguishes from siblings like ask_pipeworx and validate_claim by focusing on bet 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?

Explicitly provides example use cases ('should I bet on X?', 'what does the data say?', 'is there edge?') and explains it's the core demo product, implicitly guiding when to use versus discovering packs manually.

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 data sources (SEC EDGAR/XBRL, FAERS, FDA) and return format (paired data + citation URIs), but it omits potential limitations, error handling, or performance 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 fairly detailed with multiple sentences, but every sentence adds value. It front-loads the purpose and then specifies data sources and return format. Could be slightly more concise, but structure is good.

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 (two entity types, no output schema), the description explains what data is returned for each type and the return format. It could mention error handling or missing data behavior, but overall it is sufficiently complete.

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

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

Schema coverage is 100%, but the description adds significant context beyond the schema: it explains the meaning of the 'type' parameter and provides format examples for 'values' (tickers vs drug names) and the allowed range (2–5).

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: comparing 2–5 companies or drugs side by side. It provides specific use cases and data sources for each entity type, making it distinct from sibling tools like '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?

The description gives explicit when-to-use scenarios (e.g., 'compare X and Y', 'which is bigger') and notes it replaces multiple sequential calls. However, it does not explicitly mention when not to use it or alternative tools.

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

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

No annotations are provided, so the description carries the burden. It discloses that the tool returns 'the most relevant tools with names and descriptions', which is sufficient behavioral transparency. However, it does not mention any side effects or performance characteristics, but given the read-only nature implied by 'Search', this is adequate.

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

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

The description is only three sentences, each essential. It states the core function, what it returns, and when to call it. No wasted words or redundancy.

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

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

Given the tool's simplicity (2 params, no output schema, no nested objects), the description is adequately complete. It covers purpose, usage guidance, and parameter semantics. It could optionally mention that results are ranked by relevance, but this is not a critical gap.

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 baseline is 3. The description adds value by stating that the query should be a 'natural language description' and provides examples (e.g., 'analyze housing market trends'), which enriches the parameter's meaning beyond the schema. This warrants a 4.

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

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

The description clearly states the tool's purpose: to search the Pipeworx tool catalog by describing what you need. It specifies the action ('Search'), the resource ('tool catalog'), and the mechanism ('by describing what you need'), which is unique among siblings and provides clear differentiation.

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 advises to 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This gives a clear when-to-use instruction, and since no sibling tool performs a similar catalog search, it effectively implies when not to use others.

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 clearly lists what data is returned (SEC filings, fundamentals, patents, news, LEI) and mentions citation URIs. However, it does not disclose potential errors, rate limits, or result size, which would elevate it to a 5. Still, it provides substantial behavioral context.

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

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

The description is four sentences long with no redundant information. It is front-loaded with the purpose, followed by usage examples, return contents, and parameter details. Every sentence earns its place, achieving high density of useful information.

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

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

Although the description lists the types of data returned, it lacks details on the output structure (e.g., whether it's a flat JSON, sections, or how citations are embedded). Given the complexity of aggregating multiple sources and no output schema, a more complete description of the return format would be expected for a tool of this nature.

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 beyond the schema by explaining the 'type' enum (only 'company' supported, with person/place coming soon) and 'value' format (ticker or CIK, with advice to use resolve_entity for names). This extra context warrants a score above the baseline of 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 starts with a clear verb and resource: 'Get everything about a company in one call.' It lists specific use-case queries and explicitly distinguishes from sibling tools like resolve_entity, which is mentioned as a prerequisite for name-based lookups. This makes the purpose highly specific and differentiated.

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 when-to-use (user asks for company profile or when many tools would be needed) and when-not-to-use (when only a name is available, use resolve_entity first). It also implies the alternative of calling multiple individual tools, making the decision 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 are provided, so the description carries full burden. It only states the basic action without disclosing behavioral traits such as whether deletion is permanent, whether it requires confirmation, or what happens on success/failure. The description lacks detail on side effects (e.g., no undo) or permissions needed.

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, short sentence that is front-loaded with the verb 'Delete' and the resource. Every word is necessary and there is no filler. It is highly concise.

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

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

Given the tool's simplicity (1 parameter, no output schema, no annotations), the description is minimally adequate. However, it lacks details about error cases (e.g., key not found), return value (void or confirmation), and whether the operation is idempotent. A more complete description would mention these aspects.

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% for the single parameter 'key', which has a description in the schema. The tool description adds no additional meaning beyond what the schema already provides. Baseline 3 is appropriate since the schema already documents the parameter.

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

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

The description clearly states the action (delete), resource (stored memory), and identifier (key). It distinguishes the tool from its siblings: 'remember' creates memories, 'recall' retrieves them, and 'forget' deletes them. The purpose is unambiguous and specific.

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

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

The description implies when to use this tool (when you need to delete a memory), but provides no guidance on when not to use it or alternatives. For example, there's no mention that the memory must exist or what happens if the key is not found. No explicit exclusions or references to 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?

Annotations are empty, so the description carries the full burden. It states that the tool gets a post and top-level comments, which is transparent. However, it does not disclose any limitations (e.g., rate limits, pagination, or that only top-level comments are returned without nested replies). Given no annotations, a score of 3 is appropriate as it covers basic behavior but lacks depth.

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 that is front-loaded with the verb and resource, and includes the key input (post ID). Every word earns its place, no fluff.

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 parameter, no nested objects, no output schema), the description is complete enough to convey what the tool does and what it returns (post and top-level comments). It could mention that comments are top-level only, but that is implied. A score of 4 reflects that it is sufficient for the complexity level.

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 required parameter 'post_id' already described as 'Reddit post ID (the alphanumeric ID, e.g. "abc123")'. The description adds that the post ID is needed but does not add new meaning beyond the schema. With high schema coverage, baseline is 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 the verb 'Get' and the resource 'a Reddit post and its top-level comments', and specifies the input is by 'post ID'. It distinguishes from siblings like 'get_subreddit' which fetches a subreddit, and 'search_posts' which searches, making the purpose distinct.

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

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

The description implies when to use this tool: when you need a post and its top-level comments given a post ID. It does not explicitly exclude alternatives or mention when not to use it, but the context is clear. Sibling tools like 'search_posts' are for searching, so the guideline is adequately 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?

The description only says 'hot posts', but does not explain other possible behaviors (e.g., whether 'hot' is the only sort, if it returns a specific format, or any rate limits). Since there are no annotations, the description carries full burden but fails to disclose 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?

The description is very concise (single sentence) and to the point. It is front-loaded with the main action. However, it could be slightly expanded without losing conciseness.

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 and the existence of sibling tools for posts, the description is insufficient. It does not explain what 'hot posts' means, how the limit parameter works beyond what's in the schema, or what the response format is. No output schema exists to clarify.

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

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

The input schema has 100% coverage with clear descriptions for both parameters (limit and subreddit). The description adds no additional meaning beyond what the schema already provides, so baseline score of 3 is appropriate.

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

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

The description states it gets 'hot posts from a subreddit', which is a specific verb and resource. However, it does not distinguish from sibling tools like 'get_post' or 'search_posts', leaving ambiguity about the scope.

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. For example, it doesn't mention that other tools like 'search_posts' might be better for searching across subreddits or that 'get_post' is for a single post.

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 rate limits (5 per identifier per day), cost (free, doesn't count against quota), and how feedback is processed (daily digests, affects roadmap). No annotations provided, so description fully covers 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?

Concise single paragraph, front-loaded with purpose, every sentence adds essential 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?

Completely describes the tool's purpose, usage, constraints, and behavior. No output schema needed; the tool is straightforward. All necessary information is present.

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 three parameters with descriptions. The description adds value by instructing to describe issues in terms of Pipeworx tools/packs, which clarifies usage 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?

Clearly states the tool is for providing feedback to the Pipeworx team about bugs, features, data gaps, or praise. Uses specific verbs and resources, and distinguishes from sibling tools like discover_tools or ask_pipeworx.

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 each feedback type (bug, feature, data_gap, praise) and what not to do (don't paste end-user prompt). Provides clear context and 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?

The description aligns with annotations (readOnlyHint=true, openWorldHint=true, destructiveHint=false) by stating it only checks monotonicity and returns opportunities without mutation. It adds behavioral context about searching and grouping in topic mode, enhancing transparency without contradictions.

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

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

The description is concise yet comprehensive, with each sentence adding value. It is front-loaded with the primary purpose and structured logically: first the overall goal, then the two modes with examples. No redundant or irrelevant information.

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

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

Given the lack of an output schema, the description adequately explains return values ('ranked opportunities with suggested trade direction + reasoning'). It covers both modes and their logic, making the tool's behavior fully understandable for an AI agent.

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

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

Both parameters ('event' and 'topic') are described in the schema with good coverage (100%). The description adds significant meaning by explaining the two modes, usage examples, and the rationale for cross-event detection, going well beyond the schema's basic field descriptions.

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

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

The description clearly specifies the tool's goal: 'Find arbitrage opportunities on Polymarket by checking for monotonicity violations across related markets.' It names the specific action, resource, and method, and distinguishes it from sibling tools like 'polymarket_edges' and 'bet_research' by focusing on arbitrage via monotonicity.

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

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

The description explains two distinct modes ('event' and 'topic') with explicit guidance on when to use each. It provides an example where cross-event mode is necessary ('by May 31' vs 'by Jun 30') and warns that single-event mode misses such cases, offering clear 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?

Annotations indicate read-only and non-destructive behavior. The description adds substantive context: the model (lognormal from FRED + coinpaprika), data fetching strategy (price history fetched once), and ranking by edge magnitude. This goes beyond annotations but could mention rate limits or data staleness.

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 and front-loaded with the main purpose. Every sentence contributes useful information: purpose, scope, methodology, output. 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?

Despite no output schema, the description explains the output format (top N edges with suggested direction). It covers the algorithm, data sources, and parameter usage. Minor gaps include handling of errors or very large results, but overall adequate for a discovery tool.

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

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

Schema coverage is 100% with descriptions for all three parameters. The description reinforces the parameters (limit, window, min_edge_pp) but does not add new meaning beyond what the schema already provides. 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 scans high-volume Polymarket markets to find those with greatest disagreement between Pipeworx model and market price. It uses specific verbs and resources, and differentiates from sibling tools like polymarket_arbitrage by focusing on model-based edge for crypto bets.

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 frames the tool for the 'what should I bet on today' question, helping agents discover opportunities without manual browsing. However, it does not explicitly state when not to use it or compare with alternatives like bet_research or arbitrage.

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

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

No annotations provided, so description carries full burden. It states behavior: retrieve by key or list all. But lacks details on what happens if key doesn't exist (error vs empty?), or any side effects. Adequate but not rich.

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

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

Two sentences, no wasted words. Front-loaded with action and resource. Efficient and 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?

Given simple tool (1 optional param, no output schema), description covers core functionality. But lacks info on error cases, performance (e.g., large memory lists), or persistence. Adequate for basic 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 covers key parameter with description 'Memory key to retrieve (omit to list all keys)'. Description adds that omitting key lists all, which aligns with schema. With 100% schema coverage, description adds minimal extra meaning, baseline 3 is appropriate.

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

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

Description clearly states verb 'Retrieve' and resource 'memory by key', and distinguishes between retrieving a specific memory and listing all. Sibling 'remember' and 'forget' suggest memory operations, and this tool's purpose is distinct.

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

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

Description explicitly says 'omit key to list all keys' and mentions retrieving context from earlier sessions. However, no explicit when-not-to-use or alternatives to other tools, though context is clear.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It transparently explains that the tool fans out to SEC EDGAR, GDELT, and USPTO in parallel, and describes the return format including structured changes, total_changes count, and citation URIs. Missing details like latency, rate limits, or error handling, but the core behavior is well-covered.

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

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

The description is three sentences, each earning its place. The first sentence states the purpose with examples, the second explains the execution (fan-out to sources), and the third details parameter formats and return structure. It is front-loaded with the most critical information and contains no 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 complexity (fan-out to multiple external APIs) and the absence of an output schema, the description provides a solid overview of inputs, outputs, and behavior. It could be more complete by mentioning potential issues like partial failures, but it sufficiently informs an agent about what to expect.

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 already provides 100% coverage with descriptions for all three parameters. The description adds value by explaining the 'since' parameter format in detail (ISO date and relative shorthand with examples) and clarifying that 'value' can be a ticker or CIK. This goes beyond the schema, earning a score above the baseline of 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 the tool's purpose: to retrieve recent changes for a company from multiple sources. It provides concrete example queries, making it easy for an agent to recognize when to use this tool. It is specific about the verb (returns changes) and the resource (company over time), and it naturally distinguishes itself from sibling tools 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?

The description explicitly lists example user phrasings that should trigger this tool, such as 'what's happening with X?' and 'any updates on Y?'. This gives clear usage context. However, it does not explicitly state when not to use the tool or compare it directly with siblings, which would further strengthen this dimension.

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 that memory persists for authenticated users and 24 hours for anonymous sessions, which is helpful. However, it does not mention if values can be overwritten, size limits, or whether storing duplicate keys updates or errors.

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: first states purpose, second gives usage examples, third adds behavioral context. No wasted words, all sentences earn their place.

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

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

Given the tool has only 2 parameters, no output schema, and no nested objects, the description is fairly complete. It covers purpose, usage, and persistence behavior. Could mention if values can be overwritten or updated.

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 each parameter having a description. The tool description adds examples of key usage (subject_property, target_ticker) and notes value can store any text, but these add moderate value 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 stores a key-value pair in session memory, specifying the verb 'store' and resource 'key-value pair'. It distinguishes from siblings like 'recall' and 'forget' by focusing on saving data, not retrieving or deleting.

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 (save intermediate findings, user preferences, context across tool calls) and mentions persistence differences based on authentication. However, it does not explicitly state when not to use or name alternatives like 'recall' for retrieval.

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 the description carries full behavioral transparency burden. It states the tool returns IDs and citation URIs but does not disclose whether it is read-only, idempotent, or has any side effects. For a lookup tool, this is acceptable but could be improved.

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 unnecessary words. It front-loads the core purpose and structure, and 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 two parameters with full schema descriptions and no output schema, the description explains what the tool returns (IDs and URIs) and its usage context. It is largely complete, though the exact output format could be further specified.

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 of type and value. The description adds examples and specific input formats (e.g., ticker, CIK, brand/generic name), which clarifies usage beyond the schema alone.

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: looking up canonical identifiers (CIK, ticker, RxCUI, LEI) for companies or drugs. It provides examples and distinguishes from other tools by saying it replaces 2-3 lookup calls and should be used before tools requiring official identifiers.

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 advises using this tool when a user mentions a name and needs official identifiers, and to use it before calling other tools that require those IDs. Does not explicitly mention when not to use it or provide alternative tools, but the guidance is clear and actionable.

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

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

No annotations are provided, so the description must convey behavior. It states 'Search Reddit posts' implying a read-only operation, which is consistent. However, it does not disclose any side effects, rate limits, or whether results are paginated. The schema details are minimal but sufficient for basic use.

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 short sentence, which is concise. However, it is too minimal to be highly useful. It earns its place but could be slightly expanded to include key details like default sort or limit without being 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?

With only 3 parameters and no output schema, the description is incomplete. It does not mention the return format, error conditions, or any constraints like time range. For a search tool, this is insufficient for an agent to fully understand its behavior.

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 description adds no additional meaning beyond the schema. The description does not elaborate on parameter semantics; it only repeats the tool's purpose. Baseline 3 is appropriate given full 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: searching Reddit posts by query string. The verb 'Search' and resource 'Reddit posts' are specific, and the description distinguishes it from siblings like get_post (single post) and get_subreddit (subreddit info). However, it could be more precise by mentioning it searches by query, not by other criteria.

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

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

No guidance on when to use this tool versus alternatives like get_post or get_subreddit. There is no mention of prerequisites or when not to use it. The description is minimal and does not help the agent decide between this and other search/retrieval 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, but description fully informs: returns a verdict, extracted form, actual value with citation, and percent delta. Discloses that it replaces multiple sequential calls. No mention of destructive actions or side effects, which seems appropriate.

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

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

Two concise sentences, front-loaded with action verb. Includes examples, scope, and return structure without any wasted words.

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

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

Given single parameter (covered thoroughly), no output schema needed, and no annotations, the description fully addresses purpose, usage, constraints, and results. No 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 coverage is 100%, but description adds value by elaborating claim format as 'Natural-language factual claim' and giving examples, going 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?

Description uses specific verb 'fact-check' and states it validates natural-language claims against authoritative sources. Distinguishes by noting it replaces 4–6 sequential calls. Clearly differentiates from sibling tools like search_posts or resolve_entity.

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 an agent needs to check whether something a user said is true' with examples. Scope is clearly limited to company-financial claims for US public companies. No explicit when-not-to-use, but the scope provides implicit boundaries.

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