atlaspi-mcp

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

No annotations are provided, so the description must disclose behavioral traits. It mentions the tool returns a structured report with certain fields, but does not discuss potential errors, required permissions, or any side effects. The agent would need to assume it is safe, but lacks explicit reassurance.

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 efficiently cover action, output, and use cases. The first sentence states the core function, and the second provides examples. No extraneous information, though it could be slightly more concise by removing the redundant 'dato il loro id numerico'.

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 comparison tool with no output schema, the description outlines the return structure (temporal overlap, territorial differences, etc.). However, it omits details like output format (JSON?), handling of invalid IDs, or entity type restrictions. It is adequate but not fully explicit.

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 required parameters (id1, id2). The description adds no additional detail beyond stating they are numeric IDs. Thus, it neither improves nor detracts from the schema, earning the baseline score of 3.

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

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

Description clearly states it compares two historical entities by ID and returns a structured report with specific aspects like temporal overlap and territorial differences. Examples of comparative questions further clarify purpose. It distinguishes from siblings like get_entity (single entity) and what_changed_between (change over time).

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 'Utile per domande comparative' and gives examples, guiding when to use. It does not explicitly exclude cases or mention alternatives, but the context is sufficiently clear for an AI agent to infer appropriate usage.

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

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

With no annotations, the description does not mention behavioral traits like read-only nature, performance implications, or rate limits. It focuses on output content but omits side-effect disclosure. A 3 is appropriate because the tool is simple and likely safe, but transparency could be improved.

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

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

Single paragraph, front-loaded with the most important information (what it returns). Every sentence serves a purpose, no fluff. Very 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 no output schema, the description adequately lists the returned statistics. However, it does not specify if the stats are for the entire dataset or include any implicit filtering. For a zero-param discovery tool, it is mostly complete but could note response format or caching.

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

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

No parameters exist, so schema coverage is 100%. The description adds value by explaining the output structure (list of statistics) beyond the empty schema. Baseline for zero params is 4, and the description meets that.

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 aggregate statistics for the AtlasPI dataset, listing specific metrics (total entities, breakdown by type/status/continent, time range, etc.). It distinguishes its role as a discovery tool before specific queries, setting it apart from sibling tools that focus on specific entities or events.

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 'Utile come tool di scoperta iniziale per capire la copertura del dataset prima di formulare query specifiche', indicating when to use it. Missing explicit when-not-to-use or alternatives, but the context makes it clear this is a high-level overview.

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 present, so description carries full burden. It discloses the exact fields returned and the focus on the transition leading to the entity. Does not mention side effects or permissions, but as a read operation this 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?

Two sentences with no wasted words. First sentence defines output fields, second provides a practical example. 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?

For a simple tool with one parameter and no output schema, the description explains what is returned and gives a usage example. Lacks discussion of edge cases (e.g., no predecessor), but overall informative and sufficient.

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

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

Only one parameter (entity_id) with numeric type. Description adds meaning by indicating it refers to a historical entity (e.g., 'Repubblica di Turchia'). Schema coverage is 0% but the implicit context helps. Does not explicitly document parameter meaning, 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?

Description clearly states the tool returns predecessor chains with specific fields (immediate predecessor, transition_year, transition_type, is_violent, ethical_notes). Provides a concrete query example, distinguishing it from sibling tool 'entity_successors'.

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 example given: 'che entità ha preceduto la Repubblica di Turchia?'. No explicit when-not-to-use or alternative tools, 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?

No annotations are provided, so the description carries the full burden. It discloses the returned fields (successor, transition details) but does not mention side effects, auth needs, or pagination. The disclosure is adequate but not exhaustive.

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: the first gives the full functionality, the second provides an example. Every sentence adds value without redundancy.

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

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

Given the simple single-parameter input and lack of output schema, the description covers the core functionality well. It lacks details about the return structure, but the example mitigates this. Overall complete for the tool's simplicity.

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 'entity_id' is not described in the description beyond the example. With 0% schema coverage, the description should explain the parameter's meaning, but it relies on implicit understanding. The example helps somewhat but 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 explicitly states 'Restituisce le catene in cui l'entità data ha un successore' (returns successor chains with details), clearly identifying the verb and resource. It distinguishes from sibling 'entity_predecessors' by focusing on successors.

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 a concrete example 'cosa è venuto dopo Tawantinsuyu?' and implies when to use this tool. It does not explicitly state when not to use, but the sibling list provides context.

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

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

With no annotations, the description should disclose behavior. It implies a read-only operation and gives an output example, but does not discuss side effects, authentication, 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?

The description is concise with one sentence and an example. It is appropriately front-loaded, though slightly lean on detail.

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 2 parameters, the description provides a clear example but omits explanation of the optional 'region' parameter and lacks details on error handling or output structure.

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 0% and the description does not explain the 'region' parameter. The 'event_id' parameter is only implied via the example, without explicit documentation.

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

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

The description clearly states the tool returns historical epochs for a specific event, with a concrete example. However, it does not differentiate from sibling tools like list_historical_periods.

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 given on when to use this tool versus alternatives. It does not mention 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?

With no annotations, the description carries full burden. It discloses the tool is read-only, returns metadata fields (unique_dates, coverage_pct, dates list), and requires no parameters. Could mention performance, but 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?

Concise and well-structured, with front-loaded purpose. Every sentence adds value without redundancy.

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

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

Completely handles zero-parameter, no-output-schema tool. Description explains return fields and usage context thoroughly.

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

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

No parameters defined; description adds meaning by noting the tool requires no parameters and serves as dataset metadata. Baseline 4 for zero parameters 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 returns dates (MM-DD) with events in the dataset, using a specific verb and resource. It distinguishes itself from sibling tools like on_this_day and dataset_stats.

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 it before on_this_day to check if a date has results, and to suggest interesting dates. Provides concrete use cases and context for when to invoke.

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

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

With no annotations provided, the description carries full burden. It discloses the tool returns all events for an entity, explains role semantics (active vs suffered), but does not explicitly state that it is a read-only operation (though implied). No contradictions.

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

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

Two sentences: first states core purpose, second provides usage guidance and role distinction. Every sentence is necessary and informative. No fluff.

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

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

Given the absence of an output schema, the description does not specify return format, but the content is well-explained. It addresses the primary use case and differentiates from many sibling tools. Slight gap on pagination or limits, but not critical.

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

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

Two parameters: entity_id (integer, required) and role (optional string). Schema description coverage is 50% (role has description). The description adds value by explaining the role parameter with examples and the active/suffered distinction. entity_id remains basic but clear.

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 all historical events where an entity appears, with an optional role filter. It uses a specific verb ('Restituisce') and resource ('eventi storici per entità'), and distinguishes from siblings like get_event (single event) and search_events (general search) by focusing on entity-specific history.

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 the tool after get_entity/search_entities and provides a concrete example question. Distinguishes between active and passive roles, guiding appropriate query formulation. Missing explicit 'when not to use', but the context is clear enough.

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

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

Discloses key behavioral traits: returns only events with coordinates, lightweight payload (10 fields), auto-expanding time window based on epoch, and no descriptions/sources. Without annotations, this carries full burden and meets it fully.

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 efficient, with each sentence adding specific information. It is not overly verbose, though it could be slightly more concise. Still, it earns a high score for clarity and structure.

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 explains the return payload (10 fields per event) and what is omitted. It covers all necessary contextual details for an agent to understand and invoke 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 coverage is 100% with descriptions for all three parameters. The description adds value by explaining auto-expansion logic for the window parameter and why events without coordinates are excluded, providing context 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 it retrieves geolocalized historical events optimized for map display, with specifics like lightweight payload (10 fields per event) and auto-expanding window. It distinguishes itself from siblings by focusing on map visualization and coordinate-only events.

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 this tool: when user asks about events near a place in a year or to populate a map. Also states what it doesn't include (descriptions/sources) and directs to get_event for details, offering clear guidance on 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?

No annotations are provided, and the description does not disclose behavioral traits such as read-only nature, authentication requirements, or rate limits. For a stats tool, it likely reads data, but this is not stated. The description lacks transparency beyond 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?

The description is two sentences, concise and front-loaded with the key information: the specific counters and their utility. Every word earns its place; no fluff.

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

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

Given no output schema and no annotations, the description is adequate for a simple stats tool. However, it lacks details on return format (e.g., a single object or array) and any behavior notes. Some gaps remain, but the core purpose is clear.

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 no parameters (100% coverage), and the description adds meaning by listing the types of counts returned (total, by_status, etc.), which effectively describes the output. This goes beyond the empty 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 provides aggregated counters on feedback, listing specific breakdowns (total, by_status, etc.) and its utility for understanding community activity. It distinguishes itself from sibling tools like list_feedback (individual items) and submit_feedback.

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 the tool is useful for understanding community activity and identifying hot categories, which implies usage for aggregate analysis. However, it does not explicitly state when not to use it or compare to alternatives like list_feedback.

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 the weighted scoring formula and specifies that the result includes id, name, type, period, and score. This is transparent for a similarity search tool, though it omits details like pagination or 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?

The description is a single paragraph that starts with the core purpose, then details the scoring, and ends with examples. It is well-structured and information-dense, though slightly verbose (4 sentences) and could be tightened.

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, the description compensates by listing the output fields (id, nome, tipo, periodo, punteggio). The parameter coverage is complete, and the scoring algorithm is explained. No critical information appears missing for a similarity search 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 each parameter already has a schema description. The tool description adds context by reiterating the score range (0.0-1.0) and noting the output includes the score, but does not significantly enhance parameter understanding beyond the schema.

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

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

The description clearly states the tool finds similar historical entities and orders them by a similarity score (0.0-1.0). It breaks down the scoring weights (35% entity type, 30% temporal overlap, etc.) and gives example queries like 'quali imperi erano simili a Roma?' which differentiate it from sibling tools such as compare_entities or fuzzy_search.

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

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

The description explicitly provides three example usage scenarios (e.g., 'trova stati paragonabili al Sacro Romano Impero'), making it clear when to invoke this tool. It does not explicitly state when not to use it, but the examples serve as sufficient 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 the full burden. It describes the output structure (stream with 'kind' discriminator, counts per kind) and the optional inclusion of event links. It does not mention side effects, but as a read-only retrieval tool, that is acceptable. A lower score would require missing critical behavioral info.

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

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

The description is two sentences long, front-loaded with the main purpose, and every sentence adds value. It is concise with no wasted words or redundancies.

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 2 parameters and no output schema, the description explains the output structure (kind field, counts, unified stream) which is sufficient for basic understanding. It could mention potential data limits or performance, but the coverage is adequate for typical use.

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

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

Schema description coverage is 100% (both parameters have descriptions). The tool description adds context about the unified output but does not enhance parameter semantics beyond what the schema already provides. Baseline 3 is appropriate.

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

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

The description clearly states the tool returns a unified timeline for an entity, specifying the three types of events (event, territory_change, chain_transition) and explicitly differentiating from sibling tools like get_evolution and events_for_entity. The verb 'restituisce' (returns) is specific and the resource 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?

The description explicitly states when to use this tool ('instead of calling get_evolution + events_for_entity + entity_predecessors + entity_successors separately') and provides an example query ('raccontami tutta la storia dell'Impero Romano d'Oriente'). This gives clear 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 provided, so description carries full burden. It discloses algorithm (difflib.SequenceMatcher), cross-script capability, and threshold behavior via min_score. Does not mention sorting or performance, but search tool traits are clear.

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

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

Well-structured: purpose first, then technical details, then usage guidance. Every sentence adds value. No redundant phrasing.

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; description doesn't specify return format (assumes entities with scores). Lacks mention of ordering or pagination. Minor gap for a search 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%, baseline 3. Description adds value by explaining algorithm, query flexibility, and threshold interpretation. Fewer param examples needed, but overall meaningful 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 it performs fuzzy search on historical entity names, tolerating spelling errors and cross-script matching. Examples (safavid, Constantinople) illustrate purpose. It distinguishes from search_entities explicitly.

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

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

Explicitly tells when to prefer this tool: when search_entities fails due to non-standard transliteration or approximate spelling. No explicit when-not, but the context implies exact matches go elsewhere.

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 only details output fields (transition_type with ethics note, is_violent) but omits read-only nature, error behavior, 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 concise sentences: one describing output, one giving usage. Front-loaded with purpose; no wasted words, though could potentially be trimmed slightly.

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 output structure and usage hint but lacks error handling, pagination info, or response format for large chains. Adequate but with noticeable gaps.

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

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

Single parameter chain_id with basic schema (integer, minimum 1) but 0% schema description coverage; description adds no input meaning or context 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?

Description clearly states it returns details of a succession chain with chronological links, specifying fields. 'Usa dopo search_chains' differentiates from sibling search 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?

Explicitly directs to use after search_chains, providing clear context and ordering among siblings. However, no mention of when not to use or alternatives.

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

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

Discloses ethical renaming behavior (ETHICS-009) and lists returned fields. With no annotations, description carries the burden, and it does so adequately by explaining the output structure and special 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?

Two sentences, each earning its place: first sentence details output fields, second gives usage context. Slightly long first sentence but still concise overall.

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

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

Given no output schema and no annotations, the description provides a good overview of the return structure and usage context. Missing error handling but adequate for a simple lookup 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 0% coverage, and the description does not explain the city_id parameter beyond what the schema provides (integer, minimum 1). The description focuses on output rather than input semantics.

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 details of a historical city, listing specific fields like name, linguistic variants, coordinates, etc., and explicitly says to use after search_cities, 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 says 'Usa dopo search_cities per approfondire' (use after search_cities for details), providing clear when-to-use context. No when-not-to guidance, but the context is sufficient 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 provided, so description carries full burden. It details returned fields and includes an ethical guideline (ETHICS-007), though it omits error handling or authentication needs.

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 front-loaded with key information and remains concise. The ethical note is valuable but slightly extends length without being verbose.

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

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

Given the simple tool (1 param, no output schema), the description adequately details output fields and ethical notes. Missing error handling info but minimal impact for a 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?

Schema description coverage is 0%; the description only mentions 'dato il suo id' without adding meaning beyond the schema. No examples or format details are provided.

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

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

The description clearly states the tool retrieves full details of a historical event by its ID, listing specific fields. This distinguishes it from sibling tools like search_events or on_this_day.

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

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

The description implies usage for fetching a single event by ID but does not explicitly state when to use it versus alternatives or provide any exclusionary 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 must carry full behavioral disclosure. It details output fields and event types, but does not mention ordering, pagination, data freshness, or whether all events are included. Some behavioral traits are left unspecified.

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

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

The description is a single paragraph that front-loads the main purpose. It is reasonably concise and covers key aspects without unnecessary text.

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

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

Given the simple schema and no output schema, the description provides details about return fields and event types, which is valuable. It conveys the tool's purpose and output structure, making it mostly complete 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 entity_id described in the schema. The description adds no extra information about the parameter itself, only about the output. 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 returns a timeline of territorial changes (verb+resource) and lists specific event types (annexations, losses, etc.) and output fields (year, description, source, acquisition_method). This distinguishes it from siblings like get_event or full_timeline_for_entity.

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

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

The description explains what the tool does and mentions it is important for reconstructing historical arcs, but does not explicitly state when to use it versus alternatives like entity_predecessors or full_timeline_for_entity. Usage context is implied but not definitive.

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 lists returned fields but lacks disclosure on side effects, idempotency, or rate limits. Acceptable for a read operation but minimal.

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 enclosing key fields; concise and front-loaded, though slightly wordy with example.

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 main return fields for a simple lookup tool; no output schema but description compensates. Could mention error handling for invalid IDs.

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 100% with parameter description 'ID numerico del periodo.' The tool description adds no extra semantic value beyond stating it's a numeric ID.

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

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

The description clearly states the tool retrieves a full detail of a historical period by numeric ID, distinguishing it from siblings like 'list_historical_periods' and 'get_historical_period_by_slug'.

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?

Implied usage through listing included fields (description, historiographic note, alternative names, sources), but no explicit when-to-use vs 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?

No annotations provided; description does not disclose safety, authentication, or error behavior. Only implies read operation.

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

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

Single sentence with a comparative note, no wasted words. Front-loaded with 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?

Simple tool with one parameter; schema and description are sufficient for selection. Missing output schema but return value is implied.

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

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

Schema covers slug with pattern and length; description adds examples but little extra meaning. Schema coverage is 100%, baseline is 3.

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

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

Description clearly states the tool retrieves detail of a historical period by slug, and distinguishes it from get_historical_period when ID is not known.

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 slug is known but ID is not, and directly compares with sibling tool get_historical_period.

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 implies read-only behavior ('dettaglio') and discloses sensitive content (ethical notes about slave trade). However, it does not explicitly state that the tool is non-destructive or require authentication, leaving some gaps for a new user.

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 compact single sentence listing key output fields. It is front-loaded with the core purpose. The inclusion of a specific ethical note adds value without excessive length, though it could be 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 there is no output schema, the description serves as the sole documentation of return fields. It covers most vital elements, but omits input explanation and does not describe pagination or response format. For a simple get-by-ID tool, it is adequate but has gaps.

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

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

The input schema has one required parameter (route_id) with no description. The tool description adds no information about this parameter; it only describes the output. With 0% schema coverage and no parameter explanation, this dimension is severely lacking.

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 details of a commercial route, listing specific fields like waypoints with coordinates, commodities, and ethical notes. It distinguishes from siblings like search_routes or get_chain by focusing on a single route's full detail, but it does not explicitly mention the input parameter, which slightly reduces 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 search_routes (for finding routes) or get_chain (for chain details). The description does not include when/why to use it, leaving the agent to infer from the name and siblings.

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 mentions 'public transparency' but does not disclose behavioral details like pagination behavior, rate limits, or what fields each feedback item contains. Adequate but not rich.

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

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

Two sentences: first states function, second gives use case. 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?

For a 6-parameter read tool with no output schema, the description is adequate but lacks detail on return format. It covers the purpose and filtering well but is incomplete about what the response contains.

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 tool description repeats filtering categories already in schema but does not add new semantics beyond saying it's for cross-validation. 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 lists existing feedback from other agents/humans for cross-validation. It distinguishes itself from sibling tools like submit_feedback and feedback_stats by focusing on reading existing feedback.

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

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

Explicitly recommends using this tool before submitting new feedback to check for duplicates ('cross-validation'). Also lists filtering options (status, category, entity_id, submitter_type) providing clear usage context.

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

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

Discloses key traits: each period has explicit regional scope (e.g., 'Middle Ages' is European, not global), confidence_score, and historiographic_note for controversies. No annotations provided, so the description carries the full burden; it covers safety and ethics well.

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, well-structured, and front-loaded with the main purpose. It includes filtering, usage examples, and ethical notes without redundancy. Every sentence adds value.

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

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

Given no output schema, the description mentions return fields (regional scope, confidence_score, historiographic_note) but does not fully enumerate all fields. The tool has 6 parameters and no output schema, so more detail on return structure would improve completeness.

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

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

Only 3 of 6 parameters (year, region, period_type) are described in the schema. The description adds meaning for these (e.g., year returns periods including that year, region lists allowed values, period_type explains each enum) but does not address limit, offset, or status. Schema coverage is 50%, so the description partially compensates but not fully.

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 lists structured historical periods with examples (Bronze Age, Classical Antiquity) and features like regional scope, confidence score, and filtering. It distinguishes from siblings like get_historical_period by focusing on listing multiple periods.

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 scenarios are given: 'inquadrare un anno o entità nel contesto storico' with example questions. It explains filtering options but does not explicitly contrast with alternative tools like get_historical_period or event_periods.

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

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

Given no annotations, the description discloses key behaviors: haversine distance calculation, client-side sorting, year filtering based on founded/abandoned dates, and use of a separate dataset. However, it does not describe the output structure (e.g., fields returned) or error handling, 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 concise, well-structured, and front-loaded with the core purpose. Every sentence adds value: core function, examples, and sibling differentiation. No waste.

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

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

Given no output schema and no annotations, the description covers the tool's purpose, usage, and key behaviors. It lacks details on the exact return format (e.g., what fields are in each city object) but is sufficient for an agent to use the tool effectively.

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

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

Schema coverage is 100%, but the description adds context such as 'client-side sort' for max_candidates and example values. This provides more meaning than the schema alone, justifying a score above baseline.

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

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

The description clearly states the tool finds nearest historical cities to coordinates, with optional filters. It distinguishes from the sibling tool 'nearby_entities' by specifying that this tool is for historical cities, not capitals of empires/kingdoms.

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 states when to use this tool vs. 'nearby_entities'. It also explains the filtering by year and city type, giving clear context for 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?

With no annotations provided, the description carries full burden. It discloses that the tool retrieves events across all years for a given date, and gives an example output. It does not mention potential side effects, auth needs, or response structure, but the behavior is adequately conveyed for a simple query tool.

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

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

The description is concise with three sentences, each serving a purpose: stating the function, specifying format, and giving usage examples. No redundant information.

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

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

Given the simple single-parameter input and no output schema, the description is complete. It explains what the tool does, the input format, and a concrete example, covering all necessary information for correct invocation.

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% for the single parameter, which already provides format and pattern. The description adds value by providing example values ('07-04', '10-12') and explaining the context of its use, going beyond the schema's basic description.

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

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

The description clearly states the tool returns historical events for a specific date (month-day). It uses specific verb 'restituisce' and resource 'eventi storici', and distinguishes from sibling tools like search_events or get_event by its date-based 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?

The description provides clear usage context: for historical curiosities, ephemerides, quizzes, and 'accadde oggi' content. It does not explicitly state when not to use or mention alternatives, but the single-purpose nature makes it straightforward.

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 accurately describes the tool as returning a random entity and hints at optional filtering. The behavior is straightforward and unlikely to have side effects, so the description is sufficiently transparent.

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

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

The description is concise, front-loaded with the main action, and includes an example in a single paragraph. Every sentence contributes useful information without redundancy.

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

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

Given the tool's simplicity and the absence of an output schema, the description covers the essentials: what it does, when to use it, and an example. It could be considered complete for a random retrieval tool, though it does not detail return format.

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

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

Schema coverage is 100%, so the schema itself documents parameters well. The description adds value by providing a concrete example (type='khanate', continent='Asia') and contextualizes usage, which compensates for any lack of additional detail.

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 random historical entity with optional filters by type, year, continent, or status. It provides specific use cases like exploration, suggestions, and generating curiosity, distinguishing it from sibling tools that are deterministic or focused on specific queries.

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

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

The description explicitly mentions when to use the tool (exploration, suggestions, contextual trivia) and provides an example. However, it does not explicitly state when not to use it or list alternative tools, though the random nature inherently differentiates it from siblings.

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 adds ethical context about transition types and IDEOLOGICAL warnings but fails to disclose read-only nature, auth requirements, rate limits, or side effects. The tool is likely read-only but not stated.

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

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

The description is a single paragraph that is moderately concise. It front-loads the main purpose and includes relevant ethical notes. Could be slightly more structured but not overly verbose.

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

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

With 6 parameters, no required params, and no output schema, the description lacks return format, pagination behavior, and usage context. It is incomplete for an agent to correctly invoke the tool without additional assumptions.

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 50%. The description mentions chain_type, region, year, status filters but does not explain limit/offset. The ethical note on chain_type adds some value, but overall the description does not fully compensate for undocumented 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 identifies the tool as a search for succession/dynastic/colonial chains with specific filters. It distinguishes the resource 'chains' from sibling tools like 'search_events' and 'search_cities', though it does not explicitly differentiate from 'get_chain' or 'entity_predecessors'.

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 lists filters but provides no guidance on when to use this tool versus alternatives such as 'entity_predecessors' or 'entity_successors'. No when-to-use or when-not-to-use information is given.

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 explains that cities can survive multiple political entities, but lacks disclosure of auth needs, rate limits, default behavior (e.g., no parameters), or performance. The tool appears to be a read-only search, but not explicitly stated.

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

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

The description is a single paragraph of three sentences, front-loaded with the main action and filters. It is efficient with no obvious redundancy, though a slightly more structured format could improve 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?

With 7 parameters and no output schema or annotations, the description covers main filters but omits return value details, pagination behavior, and default search scope. Adequate but not fully comprehensive.

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

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

Schema coverage is 71% (high), so baseline is 3. The description lists filter types ('anno di attività', 'tipo funzionale', etc.) which map directly to schema properties. It adds no significant meaning beyond schema descriptions, except the contextual note about political entities, which is not parameter-specific.

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 searches historical cities with specific filters (year, functional type, political entity, bbox). It explains the distinction from capital_* entities, but does not explicitly differentiate from sibling search tools like 'fuzzy_search'.

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

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

The description implies usage via listing filters but provides no explicit guidance on when to use this tool versus alternatives, nor any prerequisites or exclusions.

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

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

No annotations exist, so the description must carry full behavioral burden. It discloses ethical constraints (ETHICS-007, ETHICS-008) and the purpose of known_silence. However, it does not mention pagination behavior (limit/offset), result ordering, error handling, or whether the tool is read-only (implied but not stated).

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

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

The description is three sentences, each serving a distinct purpose: core functionality, ethical constraint, and example usage. No redundant phrases, front-loaded with the main purpose. Efficient and well-structured.

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

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

Given the tool has 7 parameters, no output schema, and 32 siblings, the description covers the main filtering dimensions and ethical considerations. However, it omits details about return format, pagination specifics, and how to best combine filters for complex queries. This leaves some gaps for an agent to infer.

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 86%, high enough that the baseline is 3. The description adds value with the example query and the ETHICS-008 explanation for known_silence, but it does not fully compensate for the missing schema descriptions of approximately 14% of parameters. The added context is moderate but not transformative.

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 searches historical events with filters on year, type, status, and documented silence. It uses a specific verb ('cerca') and identifies the resource ('eventi storici'). While it doesn't explicitly differentiate from all 32 sibling tools, the filter-based search purpose is distinct enough from single-event lookup (get_event) or relationship-focused 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 provides a concrete example query ('quali genocidi sono registrati nel XX secolo?') and explains the known_silence filter's historical context. It implies usage for filtered historical searches but does not explicitly state when to avoid this tool (e.g., for entity-specific events, use events_for_entity) or mention any prerequisites.

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 adds ethical context for the involves_slavery parameter and a historical note about 'Silk Road'. However, it does not discuss authentication, rate limits, 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?

The description is relatively concise but packs in key information: filter capabilities, ethical flag, and a historical caveat. It is front-loaded and avoids 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?

Given no output schema, the description does not explain return values or result structure. For a search tool with 6 parameters, it covers the main search dimensions but leaves the response format unspecified.

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

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

Schema description coverage is 67% (4 of 6 parameters described). The description adds value by clarifying the ethical implications of involves_slavery and distinguishing route_type values (e.g., CARAVAN vs LAND). This goes beyond the schema notes.

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 searches historical trade routes with specific filters (year, route_type, involves_slavery). It distinguishes itself from sibling tools like get_route (single route) or search_events by focusing on trade routes.

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

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

The description implies usage for searching routes with given criteria, but it does not explicitly state when to use this tool versus alternatives like get_route or what not to use it for. No exclusions or conditions are provided.

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

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

No annotations are provided, so the description must carry the behavioral burden. It explains the year range logic and negative years for BC, but lacks details on result limits, ordering, or potential performance constraints.

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 plus a reminder are efficient and front-loaded with purpose. However, the reminder could be integrated or made more natural.

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?

Adequate for a filtered-listing tool with 3 parameters and no output schema. Provides core functionality but omits details like pagination, result format, or error cases.

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 value by explaining that entities are filtered by year_start≤year≤year_end and giving examples for type parameter. It mentions continent macro-regions, though not a complete list.

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 a world snapshot for a given year, listing all geopolitical entities active that year, with optional type and continent filters. This distinguishes it from sibling tools like get_route or where_was.

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 usage examples (e.g., 'che mappa politica c'era nel 300 a.C.?') and clarifies when to use. However, it does not mention when not to use or suggest alternatives.

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

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

With no annotations provided, the description carries the full burden of behavioral transparency. It explicitly states that the tool does not modify data directly, that feedback goes into a 'pending' state, and that human review is required for historical content (ETHICS). It also specifies conventions for submitter identification (e.g., model name for AI agents). This provides comprehensive insight into the tool's behavior.

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

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

The description is concise and well-structured. It front-loads the purpose, then lists usage scenarios, and ends with best practices. Every sentence serves a purpose, and there is no redundancy. The structure makes it easy for an AI agent to quickly grasp when and how to use the tool.

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 (12 parameters, 2 required) and the absence of an output schema and annotations, the description provides a thorough explanation of the feedback process, categories, required fields, and best practices. It covers the submission workflow and expectations. However, it does not specify what response the user receives after submission (e.g., feedback ID), which would be beneficial for completeness. Overall, it is sufficient but slightly lacking in this area.

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 value beyond the schema by explaining the categories and providing best practices, such as always including citation and reasoning. However, it does not provide further detail on individual parameters beyond what is already in the schema. The addition of context for submitters (e.g., 'for AI agents: provide model name') justifies a score of 4.

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

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

The description clearly states the tool's purpose: submitting structured feedback on the AtlasPI database, including data corrections, missing citations, imprecise polygons, bias reports, and missing entities. It uses the verb 'Sottometti' (submit) and specifies the resource, effectively distinguishing it from sibling read/query tools like list_feedback and feedback_stats.

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 scenarios for using the tool: finding a wrong date with a source, adding an academic citation, noticing bias, identifying a missing entity. It also offers best practices, such as always providing a citation and reasoning. Although it doesn't explicitly state when not to use it, the context implies it should be used only with verified information. The description also clarifies that feedback does not modify data directly and enters a pending state requiring human review.

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

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

No annotations provided, so description carries full burden. It mentions it's a client-side composition of two snapshots and that it's cheaper when only IDs and names are needed, but doesn't detail rate limits, permissions, or exact output structure.

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

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

Description is concise—3-4 sentences—all sentences are informative: purpose, examples, efficiency note. No redundancy.

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

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

Given no output schema, the description adequately explains what is returned (appeared/disappeared entities, persistent IDs) and optional filters. It could be more precise about the response shape but covers the main aspects.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds context about optional filters and usage examples but does not provide significant new details beyond the schema descriptions.

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

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

The description clearly states the tool computes a diff between two years, returning entities that appeared, disappeared, and persistent IDs. It explicitly distinguishes from a sibling tool (snapshot_at_year) by noting it's a client-side composition cheaper than two snapshot 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 provides concrete example queries and notes when the tool is more economical than calling two snapshots. It lacks explicit 'when not to use' guidance but the context is clear.

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

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

No annotations are provided, so the description must carry the full burden. It discloses behavior for disputed territories (returns all claimants with status='disputed') and explains the two modes. However, it does not specify return format, error handling, or performance considerations.

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

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

The description is somewhat lengthy, especially due to the Italian text, but it is structured with clear sections (version, purpose, modes, examples). Every sentence adds useful context, though it 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 has 4 parameters, no output schema, and no annotations, the description provides adequate context: use cases, two modes, handling of disputed territories. Missing details like return format and limitations, but overall sufficient for an AI agent to understand the tool's purpose and basic behavior.

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

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

Schema coverage is 100%, so parameters are already well-defined. The description adds value by explaining the distinction between 'year' and 'include_history' modes, and provides examples of year values (e.g., -300 for 300 BC). This goes beyond the schema's formal 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 defines the tool as a reverse-geocoding temporal tool that returns historical entities controlling a given point in a given year. It provides specific examples of use cases (genealogy, diaspora) and differentiates itself from siblings by focusing on historical boundary querying.

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 with example queries like 'who ruled Istanbul in 1500?' and contrasts the two modes (year-specific vs include_history). However, it does not mention when to avoid using this tool or compare it directly with alternative sibling tools.

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