Ship On Friday

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

The description discloses that the tool picks the best data source, fills arguments, and returns results, implying it may invoke other tools. It does not require annotations since no annotations are provided, and the description adds value by explaining the autonomous behavior. However, it does not discuss limitations, error handling, or potential side effects.

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 four sentences to convey purpose, behavior, and usage. It is front-loaded with the key action and resource, and includes examples without unnecessary detail. Every sentence serves a purpose.

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

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

Given the tool has a simple single-parameter input, no output schema, and no annotations, the description provides sufficient context for the agent to use it correctly. It explains the tool's autonomous behavior and gives examples. A score of 5 would require more detail on return format or error handling, but the current level is adequate.

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

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

The input schema has 100% coverage for the single parameter 'question', so the baseline is 3. The description adds context by specifying the parameter should be a natural language question and provides examples, which is helpful but not essential since the schema already describes it as 'Your question or request in natural language'.

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

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

The description clearly states the tool accepts natural language questions and returns answers from the best available data source. It specifies the verb 'ask' and resource 'pipeworx', and distinguishes itself from siblings by explaining it selects and uses other tools internally, unlike sibling tools like discover_tools or recall.

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: when you want to ask a question in plain English without browsing tools or learning schemas. It does not explicitly exclude scenarios, but the context implies it is a general-purpose query tool. The absence of alternatives or when-not-to-use scenarios prevents a score of 5.

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 (readOnly, openWorld, non-destructive) are complemented by detailed behavioral description: resolves market, classifies bet (lists categories), fans out to relevant packs (gives examples), returns evidence packet plus comparison. No contradictions, and the description adds significant context beyond 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 front-loaded with the core purpose and includes necessary details without extraneous text. It contains about six sentences, each adding value, but could be slightly more streamlined.

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 (multiple packs, classification, comparison) and lack of output schema, the description provides a solid overview of behavior and return structure. However, it could elaborate on the exact format of the 'evidence packet' or comparison 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 describes two parameters with 100% coverage. The description adds significant meaning: market can be slug, URL, or question text; depth enum values are explained ('quick = 2-3 evidence sources, thorough = full fan-out') and default behavior is stated ('Default thorough').

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

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

The description clearly states the tool's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies input types (slug, URL, question text) and output (evidence packet + comparison), and differentiates from siblings by positioning it as the core demo product for betting research.

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

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

Explicit usage examples are provided: 'Use for "should I bet on X?", "what does the data say about this Polymarket market?", or "is there edge in this bet?"' It explains the tool's internal process and hints at alternatives (agents that discover packs themselves convert less), but lacks explicit when-not-to-use conditions.

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 data (paired data + URIs) but does not disclose read-only nature, authorization needs, or potential side effects, despite no annotations to rely on.

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, front-loaded with core action, no superfluous words. Highly efficient.

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

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

No output schema; return description is vague ('paired data'). Lacks detail on data structure or pagination, though adequate for a straightforward comparison 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 already covers both parameters with descriptions; the description adds context about efficiency but not additional semantic depth beyond what 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?

Clearly states it compares 2–5 entities side by side, with specific metrics per type (company vs. drug). Distinguishes from sequential calls, making purpose unmistakable.

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 mentions when to use (comparing entities) and efficiency gain, but does not specify when not to use or direct alternatives from sibling tools.

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

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

No annotations are provided, so the description must convey behavioral traits. It states that it returns 'the most relevant tools with names and descriptions' and implies it is a search operation. However, it does not disclose whether the tool is read-only, whether it has side effects, or any rate limits. With no annotations, more detail would be beneficial, but the description is not misleading.

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-loading the core purpose and a clear usage directive. Every sentence adds value: the first states the action, the second describes the output, the third gives when-to-use guidance. No wasted words.

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

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

Given that there is no output schema and only two parameters (both well-described), the description is largely complete. It explains the tool's purpose, input, output, and usage context. It could be improved by mentioning whether results include tool IDs or how to subsequently invoke them, but it is sufficient for an agent to use the tool 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%, so the schema already documents both parameters. The description adds value by providing an example query ('analyze housing market trends'), which clarifies the intended use of the 'query' parameter. For 'limit', it restates the default and max values (already in schema) but no additional semantics. Overall, the description enhances 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's purpose: to search the Pipeworx tool catalog by describing what you need and return the most relevant tools. It specifies the action ('Search'), the resource ('Pipeworx tool catalog'), and the input type (natural language description). This distinguishes it well from siblings like ask_pipeworx (which likely answers questions) or recall/remember (which handle memory).

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 tells when to use this tool: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This provides clear usage guidance, implying that for a large toolset, discovery should precede invocation. No alternatives are explicitly named, but the context of sibling tools and the description's directive are sufficient.

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 carries full burden. It lists data sources returned (SEC filings, XBRL, patents, news, LEI) and notes citation URIs. Lacks explicit mention of read-only nature or any rate limits, but covers key 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?

Packed with information in a concise format. Front-loaded with main purpose, includes specific data sources, alternative tool mentions, and format details. No unnecessary words.

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

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

For a complex multi-source tool, the description is quite complete. Details data sources, output format, and type limitations. Lacks mention of result limits (e.g., number of patents/news articles) or time ranges, but overall sufficient for agent selection.

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%. Description adds meaning beyond schema: explains type enum ('company' only) and value field (ticker or CIK, with name resolution redirect). Provides actionable guidance on using resolve_entity for names.

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 profile of an entity across multiple Pipeworx packs, with specific details for type='company'. It differentiates from sibling tools by noting it replaces 10-15 sequential calls and directs to 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?

Explicitly says when to use (comprehensive entity profile) and when not (federal contracts, use usa_recipient_profile). Also advises using resolve_entity first if only have a name, providing clear alternative guidance.

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 deletion but does not disclose whether deletion is irreversible, whether it affects other data, or any side effects.

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

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

Single sentence, zero waste. Every word is necessary and directly conveys the action.

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 required param, no output schema), the description is adequate but lacks behavioral details (e.g., what happens if key doesn't exist) that would make it fully 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%, and the description adds no additional meaning beyond what the schema's parameter description ('Memory key to delete') 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 uses the specific verb 'Delete' and the resource 'stored memory by key', clearly distinguishing it from siblings like 'recall' (retrieval) and 'remember' (storage).

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 explicit when-to-use or when-not-to-use guidance is provided, but the purpose is clear enough that an agent can infer it is for deletion when a memory is no longer needed. No alternative tools 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?

With no annotations, the description carries full burden. It discloses the rate limit (5 per day per identifier) and provides behavioral guidance (avoid including prompt verbatim). It could be improved by noting side effects (e.g., feedback visibility, response time).

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, front-loaded with purpose, and contains no superfluous information. Every sentence adds value (purpose, guidelines, rate limit).

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 feedback tool with no output schema, the description covers all essential aspects: purpose, usage scenarios, content guidelines, and rate limiting. It is complete for its complexity.

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

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

Schema description coverage is 100%, so each parameter is already documented. The description adds valuable extra guidance (do not include end-user prompt), enhancing parameter semantics 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's purpose as sending feedback to the Pipeworx team, listing specific use cases (bug reports, feature requests, etc.). This verb+resource combination is distinct from sibling tools, which serve different functions.

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

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

The description provides clear when-to-use guidance (bug reports, feature requests, etc.) and includes important behavioral instructions (describe what you tried, do not include end-user prompt). However, it lacks explicit when-not-to-use or alternative tools, which would make it a 5.

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 elaborates on the internal logic (walking child markets, extracting dates, sorting, reporting violations) and provides an example. This adds significant context beyond the annotations, which already indicate read-only and open-world 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 a single coherent paragraph that efficiently conveys purpose, logic, and usage. It is slightly lengthy but every sentence adds value. Could be slightly more 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 (single parameter, no output schema), the description adequately describes the return format (list with market_a, market_b, gap_pp, suggested_trade) and the underlying logic. It is complete for an analytical read-only tool.

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

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

The single parameter 'event' is well-documented in both the schema and description. Schema coverage is 100%, and the description reinforces the usage with an example. No additional detail needed.

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. It specifies the resource and action distinctly, and the context differentiates it from sibling tools like polymarket_edges.

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

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

The description explains when to use the tool (for events with multiple time-bounded markets) and how to invoke it (pass slug or URL). It implicitly suggests when not to use it, but does not explicitly name 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 readOnlyHint=true and destructiveHint=false. The description adds detailed behavioral context: external data sources (FRED, coinpaprika), grouping by asset, fetching price history once, computing model probability, ranking by edge magnitude. 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 slightly verbose (4 sentences) but front-loads the main purpose and key details. It efficiently conveys use case, data sources, and ranking logic without excess. Minor redundancy ('returns top N ranked by edge magnitude' is stated twice).

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 format (top N markets with suggested trade direction), model sources, and ranking criterion. It covers the intended use case thoroughly for a read-only analysis tool with 3 parameters.

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

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

Schema coverage is 100% with clear parameter descriptions. The description restates default values (e.g., limit default 10, window default 1wk, min_edge_pp default 0.5) but adds no new semantics beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool scans highest-volume Polymarket markets, identifies where Pipeworx data disagrees with market price, computes edge, and returns top N ranked by edge magnitude. It specifies the scope (crypto-price bets, lognormal model from FRED + coinpaprika) and the use case ('what should I bet on today'). This differentiates it from siblings like 'polymarket_arbitrage'.

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

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

The description explicitly frames the tool for discovering betting opportunities without manual paging, but does not specify when not to use it or name alternatives. The context implies it is for broad opportunity discovery rather than detailed market analysis.

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

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

With no annotations, description must disclose behavior. It explains both retrieval and listing modes. Could add that retrieval is read-only and has no side effects, but the description is clear enough.

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

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

Two sentences: first states function, second explains when to use. No filler, front-loaded with key action.

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

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

For a simple tool with one optional parameter and no output schema, the description covers retrieval and listing, and hints at cross-session persistence. Fully adequate.

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

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

Schema coverage is 100% with one parameter 'key' already described. Description adds that omitting key lists all memories, which is not in schema description. Adds 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?

Clearly states the tool retrieves a memory by key or lists all if key is omitted. The verb 'retrieve' and resource 'memory' are specific, and the scope is well-defined.

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

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

Explicitly says when to use (to retrieve context saved earlier) and what to do if no key (list all). Among siblings like 'remember' and 'forget', this distinguishes itself clearly.

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

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

With no annotations, the description carries the full burden. It discloses parallel fan-out to three sources, return structure (structured changes + total_changes + URIs), and input format details (ISO date or relative). It does not mention rate limits or auth, but the behavioral core 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 a single concise paragraph that efficiently packs details about purpose, sources, inputs, and returns. It is front-loaded with the main action. Minor improvement could be structuring with bullets, but present format is 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 the complexity (multiple parallel sources) and lack of output schema, the description covers key aspects: sources queried, return fields, and input formats. It does not specify pagination or result limits, but for a change-monitoring tool, the core completeness is adequate.

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

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

Schema coverage is 100%, but the description adds valuable context beyond schema: examples of relative 'since' formats ('7d', '30d', '3m', '1y') and clarifications for 'value' (ticker or CIK). This enhances 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?

The description clearly states the tool's purpose: 'What's new about an entity since a given point in time.' It specifies entity type 'company' and lists specific data sources (SEC EDGAR, GDELT, USPTO), distinguishing it 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?

The description provides explicit usage suggestions: 'Use for "brief me on what happened with X" or change-monitoring workflows.' This implies when to use the tool and indirectly contrasts with static profile tools. It also notes the only supported type is 'company', aiding correct invocation.

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 persistence behavior (authenticated vs anonymous session lifetime) and that it is a write operation. It does not mention overwrite behavior on same key, which is a minor gap.

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

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

Two sentences with no redundancy. The first sentence states the action and resource, the second adds usage guidance and persistence details. Every sentence earns its place.

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

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

Given no output schema and simple input schema, the description covers purpose, usage, and persistence. It lacks details on overwrite behavior or maximum value size, but overall is sufficient for a straightforward memory 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%, so baseline is 3. The description adds value by explaining the purpose of the parameters in context (e.g., 'save intermediate findings, user preferences') and provides example keys in the schema description. This exceeds the 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 uses a specific verb ('store') and resource ('key-value pair in session memory'), clearly distinguishing its purpose from siblings like 'recall' (retrieve) and 'forget' (delete). It explicitly states it is for saving context across tool calls.

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

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

The description explains when to use this tool ('save intermediate findings, user preferences, or context across tool calls') and hints at persistence differences for authenticated vs anonymous users. However, it does not explicitly state when not to use it or mention 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?

With no annotations provided, the description carries the full burden. It discloses that the tool accepts ticker, CIK, or name and returns ticker, CIK, company name, and URIs. However, it does not mention failure modes, rate limits, or idempotency, which would enhance transparency.

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

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

The description is two sentences with no extraneous words. The first sentence states the core purpose, and the second provides details and a benefit. Information is front-loaded and efficient.

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

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

Given the tool has few parameters and no output schema, the description adequately covers inputs and outputs (ticker, CIK, name, URIs). It also explains the tool's role in replacing multiple calls, providing sufficient context for an agent.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds value beyond the schema by providing concrete examples (AAPL, 0000320193, Apple) and stating the version constraint (v1 supports only company). This helps an agent understand valid inputs.

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 resolves an entity to canonical IDs, specifies the entity type 'company' for v1, and lists accepted input formats (ticker, CIK, name). It distinguishes itself from sibling tools (ask_pipeworx, discover_tools, forget, recall, remember) which serve different purposes.

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

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

The description indicates when to use this tool by noting it 'replaces 2–3 lookup calls,' implying it consolidates multiple calls. It does not explicitly state when not to use it, but provides clear context for its application.

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

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

With no annotations, the description discloses that the tool always returns 'no' and lists return fields including risk level (always catastrophic). This goes beyond the schema, though it does not detail all potential behaviors like error handling.

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 the main purpose. Highly efficient.

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

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

Given the output schema exists and the tool is simple (always returns no), the description is mostly complete. It lacks details on error conditions or limits but is sufficient for the tool's scope.

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 each parameter is already documented. The description does not add new information about parameters 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 checks whether to ship on Friday and that the answer is always no, with specific return fields. It distinguishes from siblings by being the only Friday-check 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?

The description implies usage (checking Friday shipping) but does not explicitly state when to use vs. alternatives or provide exclusions. No sibling differentiation is 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, description carries full burden. It discloses data sources (SEC EDGAR + XBRL), return fields (verdict, structured form, actual value, citation, delta), and version limitation (v1). No contradictions with annotations as 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?

Single paragraph is efficient, covering purpose, scope, return format, and value proposition. Could be slightly more structured but no wasted sentences.

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

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

For a single-param, no-output-schema tool, description adequately explains return values and data source. Lacks mention of error handling or latency, but sufficient for basic understanding.

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

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

Only one parameter (claim) with 100% schema coverage. Description adds value via example usages, clarifying natural-language format beyond the schema's type/description.

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

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

Description clearly specifies the verb (fact-check), resource (natural-language claim against authoritative sources), and supported domain (company-financial claims for US public companies). It distinguishes from siblings by noting it replaces 4-6 sequential agent calls.

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

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

Description states supported claim domain and that it replaces sequential calls, implying efficient use. However, it lacks explicit when-not-to-use or alternatives beyond the sibling list.

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