Entso E

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

With no annotations, the description must disclose behavioral traits, but it only states the data type and granularity. It does not specify whether the data is historical, forecast, or real-time, nor does it mention any limitations, data availability, or units. Critical behavioral context is missing.

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

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

The description is a single concise sentence that conveys the core concept without redundancy. While it lacks structural elements like bullet points, it is front-loaded and efficient, earning a high score for brevity.

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 absence of an output schema and annotations, the description is critically incomplete. It does not describe the format or structure of the returned data (e.g., list of objects with fields like production type and value). An agent cannot reliably interpret the expected output from this minimal description.

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 low (33%, only 'area' has a description). The tool description does not explain the remaining two parameters ('period_start', 'period_end'), their format, or expected range. The agent must infer their meaning from the name alone, which is insufficient.

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 identifies the resource (actual generation per production type) and time granularity (per hour). It lists example production types (solar, wind, etc.) which distinguishes it from sibling tools like 'actual_load'. However, it lacks an explicit verb like 'get' or 'retrieve', which would strengthen clarity.

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

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

No guidance is provided on when to use this tool versus alternatives such as 'actual_load' or 'installed_capacity'. There is no mention of prerequisites, context, or exclusions, leaving the agent without direction on tool selection.

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

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

With empty annotations, the description should disclose more behavioral traits (e.g., time zone handling, aggregation level, data definition of 'load'). The brief sentence adds minimal beyond the name.

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

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

One efficient sentence front-loading the key info (unit per hour). However, it omits crucial usage details that could be added without verbosity.

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

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

For a tool with no output schema, no annotations, and 3 parameters with low coverage, the description lacks important context like expected output shape, time zone handling, or data sensitivity.

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 area has a description in the schema (EIC code). The description does not clarify period_start/period_end format (e.g., ISO 8601, date range constraints). Low schema coverage (33%) not compensated.

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 measures electricity consumption per hour for a bidding zone in MW, with a specific verb and resource. It distinguishes from siblings like actual_generation_per_type and day_ahead_prices by focusing on load.

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 vs siblings (e.g., day_ahead_prices, cross_border_flow). No context on prerequisites or limitations.

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

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

No annotations provided. The description lacks disclosure of behavioral traits such as whether it writes data, authentication needs, error handling, or rate limits. For a tool with no annotations, the description should provide more context about side effects and limitations.

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

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

The description is informative and front-loaded with purpose and usage. It could be slightly more concise, but every sentence adds value.

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

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

Given the complexity of routing across many sources and no output schema, the description is fairly complete about what it does and when to use. However, it lacks behavioral transparency and details about what happens if the tool cannot answer.

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

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

Input schema has one parameter ('question') with schema coverage at 100%. The description adds examples and context about the type of questions, but does not add significant meaning beyond the schema description.

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

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

The description clearly states it answers natural-language questions by automatically selecting the right data source. It provides specific examples of queries and lists over 300 sources, effectively distinguishing it from sibling tools.

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

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

Explicitly advises using when the user asks a question like 'What is X?' and when you don't want to figure out which tool to call. It implies when not to use (if you already know the tool) but doesn't state alternative tools explicitly.

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 details the internal fan-out logic, market resolution, classification, and output format. Annotations (readOnlyHint, openWorldHint) are consistent, and the description adds context beyond them.

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

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

The description is efficiently structured: front-loaded with purpose, then input formats, process, output, and usage examples. Every sentence adds necessary context without redundancy.

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

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

For a research tool with two parameters and no output schema, the description fully covers input, behavior, output (evidence packet + comparison), and use cases. It leaves no significant gaps for an AI agent.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaning by explaining the `market` parameter accepts slug, URL, or question text, and `depth` values 'quick' and 'thorough' with evidence source counts. This enriches 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 researches Polymarket bets by pulling Pipeworx data, with specific verbs and resource. It distinguishes from siblings by being the core demo product that consolidates multiple data packs into one call.

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

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

The description explicitly states when to use the tool: 'should I bet on X?', 'what does the data say?', 'is there edge?'. It implies alternatives by noting this is the preferred method over discovering packs individually.

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 data sources (SEC EDGAR/XBRL for companies, FAERS/FDA/clinicaltrials for drugs) and return type (paired data + citation URIs). Implies read-only behavior but does not explicitly state it is non-destructive. No annotations provided, so description carries the burden adequately.

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

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

Two sentences that front-load the core purpose and follow with usage guidance. Every clause adds value, no redundancy. Length is appropriate for the tool's complexity.

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

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

Covers purpose, usage triggers, type-specific behavior, and return format (paired data + URIs). Lacks explicit mention of output structure (e.g., table vs list) but given no output schema and the description's conciseness, it is reasonably complete for agent decision-making.

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 describes both parameters with 100% coverage. Description adds critical context: clarifies the interpretation of 'values' for companies (tickers/CIKs) vs drugs (names), and enumerates the specific data fields retrieved for each type (revenue, net income, etc. for companies; adverse events, approvals, trials for drugs).

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 companies or drugs side by side. Provides specific examples of user queries ('compare X and Y', 'X vs Y', 'how do X, Y, Z stack up') and explicitly contrasts with single-entity tools by noting it replaces 8-15 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?

Provides explicit context for when to use (comparison queries, tables/rankings) and distinguishes between company and drug types. However, does not explicitly list alternatives among siblings or state when not to use.

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

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

Annotations are empty, so description must supply behavioral context. It only states 'physical flow' without indicating read-only nature, data freshness, pagination, or error behavior. Minimal 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?

Single short sentence, no fluff. However, it could include more detail without harming 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?

No output schema, and description fails to mention return format (e.g., units, time resolution). With 4 required parameters and no behavioral hints, the description is insufficient for an agent to safely use the 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 has 50% description coverage (period_start and period_end lack descriptions). The tool description adds no explanation for parameters like date formatting, time zone, or required patterns. Does not compensate for schema gaps.

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 identifies the tool's purpose: reporting physical flow across an interconnector with direction (in_area → out_area). It distinguishes itself from sibling tools like 'actual_generation_per_type' or 'day_ahead_prices' which target different data.

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

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

No guidance on when to use this tool versus alternatives (e.g., for specific data types or regions). No exclusions or prerequisites 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, the description should disclose behavioral traits. It only mentions period format but omits ordering, pagination, error behavior, real-time vs historical data, or rate limits.

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, no wasted words, directly conveys the core purpose and format. 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?

Output schema is absent, so description should elaborate on return structure. It mentions 'per hour' but does not specify data format (array of objects, fields, etc.), leaving ambiguity. Incomplete for parameter count and 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 the baseline is 3. The description adds no additional meaning beyond what the schema already provides for the three parameters.

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

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

The description clearly states the tool returns day-ahead auction prices in €/MWh per hour for a bidding zone, distinguishing it from sibling tools that handle generation, load, or flows.

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 actual_generation_per_type or actual_load. The description does not mention exclusions or context for selection.

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

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

No annotations are provided, so the description must fully explain behavior. It states it 'Returns the top-N most relevant tools with names + descriptions.' However, it does not disclose how relevance is determined, potential caveats, or any side effects, leaving some ambiguity.

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, front-loaded with the core action, and covers key points without unnecessary verbosity. Could benefit from bullet points or clearer structure, but overall efficient.

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

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

Given the tool's discovery role, the description adequately covers purpose, usage, and return type. Without an output schema, it explains what is returned. The context of many sibling tools justifies its existence, but it lacks details on error handling or pagination.

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

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

Input schema coverage is 100%, so baseline is 3. The description adds example queries (e.g., 'analyze housing market trends') which adds value beyond schema definitions, but does not substantially deepen understanding of the parameters.

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

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

The description clearly states the tool's function: 'Find tools by describing the data or task.' It lists numerous domains (SEC filings, financials, FDA drugs, etc.) and distinguishes itself from sibling tools by being a discovery tool, not a specific data 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 explicitly says 'Call this FIRST when you have many tools available and want to see the option set,' providing clear guidance on when to use. It also offers context for browsing, searching, or discovering tools, though it lacks explicit when-not-to-use instructions.

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 bears full burden. It discloses the specific data returned: SEC filings, fundamentals, patents, news, LEI, and citation URIs. While it doesn't explicitly state read-only or discuss errors, it is sufficiently transparent about the tool's behavior for its purpose.

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

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

Two sentences, front-loaded with main purpose. The longer second sentence packs details efficiently. Could be slightly more structured (e.g., bullet points), but 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?

Describes all returned data categories and citation format. No output schema, but the list is comprehensive for a 'get everything' tool. Provides enough context for an agent to decide when to use it instead of multiple specialized tools.

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

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

Schema coverage is 100%, but description adds significant value: clarifies 'type' limitation to company (with future hint), explains 'value' accepts ticker or CIK but not names, and directs to resolve_entity for names. Example values enhance understanding beyond schema.

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

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

The description clearly states the tool returns comprehensive company data in one call, with specific example queries like 'tell me about X' or 'research Microsoft'. It distinguishes itself from siblings by noting it avoids calling 10+ separate tools across multiple domains.

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 context: use when a user wants a company profile, or when many individual tool calls would be needed. Provides clear guidance on accepted inputs (ticker or CIK) and directs users to resolve_entity for name-only queries.

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

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

No annotations provided, so description carries burden. It states 'delete' which implies destructive action, but lacks details on irreversibility or 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?

Two sentences, no filler. Purpose and usage are front-loaded. Every sentence adds value.

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

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

Simple tool with one required parameter. Description is sufficient for usage and behavior. No output schema needed.

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 'by key' and pairing context, but schema already describes the key parameter. Baseline 3 applies.

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

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

Description clearly states the tool deletes a memory by key, using specific verb 'delete' and resource 'memory'. It distinguishes from sibling tools 'remember' and '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?

Explicitly states when to use: context stale, task done, or clear sensitive data. Mentions pairing with remember and recall, but does not explicitly say when not to use it.

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

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

Annotations are empty, so the description carries full burden. It only states the data returned, lacking disclosure of behavioral traits such as being read-only, requiring authentication, or any potential side effects. A data retrieval tool should at least imply read-only 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, clear sentence with no superfluous words. It is efficiently front-loaded with the key information.

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

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

For a simple two-parameter tool with no output schema, the description covers the essential output characteristics. It lacks information about how production types are represented (maybe all are included) and whether data is historical or forecast, but it suffices 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 coverage is 100% with clear parameter descriptions. The description adds value by specifying units (MW) and that data is by production type, which is not in the schema. It clarifies the temporal scope (year-end). Baseline is 3, and the description contributes meaningful extra context.

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 provides year-end installed generation capacity by production type in MW. It specifies the metric (installed capacity), time (year-end), unit (MW), and that it is broken down by production type, making it distinct from siblings like actual_generation_per_type.

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

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

No explicit when-to-use or when-not-to-use guidance is given. However, the context of sibling tools (e.g., actual_generation_per_type) implies this is for capacity data versus actual generation, but the description does not mention alternatives or 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?

With no annotations, the description fully discloses behavioral traits: rate limiting (5 per day), free usage, no quota impact, and that feedback is reviewed daily and influences the roadmap. This provides sufficient transparency for the agent.

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

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

The description is a single well-structured paragraph, about 100 words, with no redundant information. It efficiently covers purpose, usage, constraints, and tips, making it easy to parse.

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

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

The description provides essential context about feedback handling (daily digest, roadmap impact) and constraints (rate limit, quota). While it doesn't mention response expectations, that is acceptable for a feedback tool. 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?

The input schema has 100% description coverage, but the description adds value by further explaining the 'type' enum and advising on specificity in the 'message'. This enhances understanding 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: to send feedback about bugs, feature requests, data gaps, or praise. It distinguishes itself from sibling tools which are data retrieval or memory tools, making its unique role evident.

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 when to use each feedback type (bug, feature, data_gap, praise) and provides anti-guidance (don't paste end-user prompt). It also mentions rate limits (5 per day) and that the tool is free, helping the agent decide when to invoke it.

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 the internal logic (walking child markets, extracting dates, sorting, checking violations) and output format, adding significant value beyond readOnlyHint annotation.

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?

Well-structured with purpose, rationale, usage, and output. Slightly verbose but clear and front-loaded.

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

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

Despite no output schema, description fully documents return format (list of dicts with keys). Completely covers input and output for a simple tool.

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

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

Single parameter 'event' is fully described in schema; description adds that it accepts slug or full URL, with an example.

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 finds arbitrage opportunities via monotonicity violations in Polymarket events, with a concrete example distinguishing it from siblings 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?

Explains to pass an event slug or URL, and describes when the tool is applicable (when multiple date/threshold markets exist). Doesn't explicitly mention when not to use or compare to alternatives, but context is clear.

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

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

Beyond annotations (readOnly, openWorld), the description details the algorithm: scanning top markets, grouping by asset, fetching price history once, computing model probability, ranking by edge, and returning top N with direction. It also discloses the model (lognormal from FRED + coinpaprika) and version (V1), providing rich 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 concise, with the first sentence stating the core purpose and the second adding algorithmic details. It is slightly dense but efficient, with no superfluous content.

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

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

For a tool with 3 parameters and no output schema, the description fully covers what the tool does, how it works (model, data sources), what inputs control (volume window, edge threshold, limit), and what output to expect (ranked edges with direction). No gaps remain.

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

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

Input schema has 100% coverage with descriptions for all three parameters (limit, window, min_edge_pp). The description does not add additional parameter-specific meaning beyond what the schema already provides, so 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 Polymarket markets and returns those where Pipeworx data disagrees most with market price, specifying the resource (Polymarket markets), the verb (scan and return), and the selection criterion (disagreement with market price). It distinguishes itself from sibling 'polymarket_arbitrage' by focusing on model-based edge discovery for crypto-price 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, indicating when to use it. However, it does not mention when not to use it or provide explicit alternatives among sibling tools, though the use case is clear.

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

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

No annotations provided, but description discloses scoping to identifier, dual behavior (retrieve vs list), and pairing with remember/forget. Does not cover rate limits 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?

Description is a single paragraph that front-loads core action, then adds context. No waste, but could be structured with bullet points for readability.

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

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

For a simple tool with one optional param and no output schema, the description covers behavior, scope, and sibling relationships adequately. Missing return format details.

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

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

Schema coverage is 100% with description on 'key' param. Description adds context on dual behavior and scope, going beyond schema basics.

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

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

Description clearly states it retrieves a saved value or lists all keys, distinguishes from siblings by explicitly naming remember and forget, and explains 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?

Provides explicit context for use (look up context stored earlier), explains how to list keys by omitting the argument, and references complementary tools, but lacks explicit when-not-to-use 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?

Without annotations, the description fully discloses the tool's behavior: it fans out to SEC EDGAR, GDELT, and USPTO in parallel, returns structured changes with counts and URIs, and explains parameter syntax (since formats). This is comprehensive and honest about external dependencies.

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 (4 sentences) yet packed with information: purpose, usage examples, data sources, parameter details, and return format. Every sentence is justified with 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 no output schema, the description adequately covers return structure (structured changes, total_changes, URIs). It includes input details and source fan-out. Could improve by providing a sample return or more detail on change types, but sufficient for agent 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?

Schema coverage is 100%, but description adds significant value: it explains 'since' accepts both ISO and relative shorthand, 'value' can be ticker or CIK, and 'type' is limited to 'company'. This enriches the bare schema descriptions.

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

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

The description clearly states the tool's purpose: answering 'what's new with a company in the last N days/months?' with concrete user query examples. It differentiates from siblings like 'entity_profile' or 'compare_entities' by focusing on temporal changes across multiple sources.

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 example queries and context for when to use ('what's happening with X?', 'news on Apple this month'). While it doesn't state when not to use it or name alternatives explicitly, the sibling tool list gives context. Close to 5 but lacks a formal 'when not to use' clause.

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 key-value scope by identifier, persistent vs 24-hour retention based on authentication. Could mention size limits but is sufficient.

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, front-loaded with purpose, then usage guidance, then behavioral details. 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?

Comprehensive for a simple key-value store. Covers purpose, usage, persistence, and pairing. Lacks explicit limits but acceptable for general 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% with rich descriptions. The description adds scoping and persistence context beyond schema, enhancing 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: 'Save data the agent will need to reuse later.' It specifies key-value storage and distinguishes from sibling tools recall and forget by mentioning them.

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

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

Explicitly provides when to use: 'Use when you discover something worth carrying forward... so you don't have to look it up again.' Also mentions paired tools recall and forget for alternative actions.

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 transparency burden. It discloses that the tool returns IDs plus citation URIs (pipeworx://), but does not mention if it is read-only, has rate limits, or requires authentication. However, the examples and purpose imply a safe, idempotent lookup, and the output format is described sufficiently for an agent.

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

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

The description is a single, well-structured paragraph that efficiently conveys purpose, usage, examples, output, and ordering. Every sentence adds value, with no fluff or repetition. It is front-loaded with the core function and provides rich detail without excessive length.

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 parameters, no output schema), the description is fairly complete: it explains what the tool does, when to use it, what inputs are expected, and what outputs are returned. It lacks explicit mention of error handling (e.g., if an identifier is not found) or edge cases, but for a lookup tool, the coverage is strong.

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 significant value by providing examples and clarifying acceptable values (e.g., tickers, CIKs, names for company; brand/generic names for drug) and explaining how the 'type' parameter constrains 'value'. This goes beyond the schema enum and string descriptions.

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

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

The description clearly states the tool's purpose: to look up canonical identifiers for companies or drugs. It provides specific examples (e.g., Apple → AAPL/CIK, Ozempic → RxCUI) and lists the ID systems (CIK, ticker, RxCUI, LEI), making the function immediately understandable and distinguishing it from general search tools.

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

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

The description explicitly directs that the tool should be used when a user mentions a name needing official identifiers required by other tools. It also states 'Use this BEFORE calling other tools that need official identifiers,' giving clear sequencing guidance. While it doesn't explicitly state when not to use it, the context sufficiently implies it's for identifier lookups only.

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 the description details the return: a verdict from a set of values, extracted data, actual value with citation, and percent delta. It also mentions it replaces multiple sequential calls, improving efficiency.

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 guidance, third details scope and outputs. No fluff, front-loaded with key information.

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

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

No output schema, but the description explicitly lists the return values: verdict list, structured form, actual value with pipeworx:// citation, and percent delta. For a single-parameter tool, this is complete.

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

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

The only parameter 'claim' has full schema coverage (100%) with a description. The tool description adds example claims beyond the schema, but the schema already documents the parameter adequately, so baseline 3.

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

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

The description clearly states the tool validates factual claims against authoritative sources, specifically company-financial claims. It is distinct from all sibling tools which handle other tasks like entity profiles or data loads.

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 a user's claim's truth. Provides example queries and notes limitations (supports only US public company financials via SEC EDGAR+XBRL), which helps avoid misuse.

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