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

With no annotations, the description fully discloses side effects: only yellow input cells on segment rows are written, blue subtotals/grey rows/demand-side rows are untouched, class-level NACE codes are resolved and flagged, and dry_run returns a plan while actual write returns a summary. This is exemplary transparency for a mutation 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 well-organized: a concise summary paragraph followed by a detailed Args bullet list. Every sentence provides useful information and avoids redundancy. Though longer than typical MCP descriptions, the complexity of the tool justifies the length, and the structure keeps it scannable.

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 covers the complete workflow, safety mechanisms (dry_run, overwrite warning), edge cases (class-level NACE resolution), and return values (plan or write summary plus plan). With an output schema present, the input side is fully specified. This is a complete and self-contained description for a complex tool.

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

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

The schema has 0% description coverage, so the description completely takes over documenting all six parameters. Each parameter is explained with defaults, examples, and special conditions (e.g., output_path overwrite warning, dry_run behavior, exclude_micro meaning). This adds immense semantic value beyond the raw schema.

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

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

The description clearly states the tool's specific action: reading NACE codes from the TAM workbook's 'Company Counts by Segment' sheet, pulling Eurostat counts, and writing to columns F and G. This distinguishes it from sibling tools which are all read-only getter tools; this is the write/fill operation.

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 strong usage context: dry_run default allows auditing before committing, warns against overwriting the original unless intended, and explains parameter behaviors. It does not explicitly name alternatives, but the sibling context makes it clear this is the only write tool. The guidance is practical and cautionary.

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 fully carries the burden. It discloses the entitlement requirement, the error behavior ('no_entitlement'), authentication via env var or .env file, and notes that employee bounds are inclusive. This is far beyond typical transparency and gives an agent critical operational knowledge.

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 well-organized with a summary, bulleted use cases, 'Args:' block, and a prerequisites paragraph. Every section serves a purpose and the front-loaded summary immediately communicates what the tool does. It is slightly long but structurally efficient and scannable.

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 covers purpose, when to use, all parameter semantics, prerequisite conditions, authentication, and error handling. Since an output schema is present, not describing return values is acceptable. It provides a complete operational picture for an agent to select and invoke the tool without guessing.

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

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

The schema has 0% description coverage, but the description compensates by explaining every parameter with examples: 'naics' with a semiconductor example, 'countries' with country lists, and 'employee_min/employee_max' with band bounds and the inclusive keyword. This fully resolves the otherwise 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 states it 'Count companies in Cognism' with specific filtering dimensions (NAICS, countries, employee range). It distinguishes itself from siblings by explaining it 'Fills the cells Eurostat/Nomis can't' and explicitly notes Cognism uses NAICS rather than NACE, which clarifies its unique role among count 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 explicit use cases ('Corp Large 5,000+', 'Corp Mid-cap 250-4,999', 'UK corporates') and a crucial prerequisite ('Requires the Cognism API entitlement'). It does not explicitly name alternative sibling tools, but the concrete examples and NAICS/NACE mapping give clear context for when this tool is appropriate.

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

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

No annotations are provided, so the description carries the full burden. It discloses defaults (EU27+NO+CH), the UK unavailability post-Brexit, the meaning of exclude_micro, the automatic latest year behavior, and the return structure including caveats. This is rich behavioral disclosure.

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 well-structured with a concise summary followed by per-argument details and a return note. It is appropriately detailed for a four-parameter tool; no sentence is wasted.

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 covers all aspects needed to use the tool correctly: parameter formats, defaults, caveats, and return values. It is complete even though an output schema exists, and it adds useful business context like the TAM sheet note.

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%, but the description fully compensates by explaining every parameter: NACE code format including valid dotted notation, geo default and country breakdown, year default behavior, and exclude_micro semantics. This exceeds what the schema alone provides.

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

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

The description clearly states the tool returns enterprise counts for a single NACE Rev.2 code, bucketed into SME and Corp, summed across specified geographies. It is specific about the resource and aggregation, but it does not explicitly contrast with sibling tools such as get_segment_counts or get_company_count, so it lacks explicit sibling differentiation.

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

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

The description gives strong context for use: it explains the single NACE code requirement, default geographies, country breakdown option, and year behavior. It does not explicitly state when not to use this tool or name alternatives, so it is a 4 rather than a 5.

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

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

With no annotations, the description carries the full burden and excels. It discloses the default geo (EU27 + NO + CH), the optional year, the effect of exclude_micro, the return structure (per-code plus segment_total), and a critical CAUTION about potential double-counting and the need for de-duplication. These are substantial behavioral traits beyond what the input schema reveals.

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 well-structured with a summary, labeled Args, Returns, and a CAUTION. Every sentence contributes essential information, with no filler. The example for nace_codes and the caution are valuable additions 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?

For a tool with 4 parameters and no annotations, the description is highly complete. It covers all parameters, return structure, default behavior, and a critical data-quality caveat. An output schema exists, so the description does not need to detail every field, but it still gives a high-level summary of per-code and segment_total results. The sibling context is implicitly addressed through the geo parameter reference and the segment-specific focus.

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 0% description coverage, so the description must compensate fully. It does: nace_codes is explained with an example, geo references get_enterprise_counts and states the default, year specifies 'single year string, or omit for latest,' and exclude_micro is described as excluding micro firms (0-9) from the SME bucket. Every parameter gains meaning not present in 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 a specific action: 'Enterprise counts for a list of NACE codes that make up one value-chain segment, returned per code AND summed for the whole segment.' This identifies both the resource (NACE-coded segment) and the unique aggregation behavior (per-code and total sum), distinguishing it from siblings like get_enterprise_counts and get_company_count. The reference to the 'Company Counts by Segment' sheet further anchors its specific use case.

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

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

The description gives clear context by stating it maps directly to a row in a specific sheet, implying when to use it. It also provides a caution about double-counting and references get_enterprise_counts for the geo parameter, signaling a related alternative. However, it does not explicitly say 'use this instead of get_enterprise_counts when you need segment totals,' so it stops short of the explicit alternatives required for a 5.

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

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

With no annotations, the description carries the full burden and does well: it discloses the HARMONIC_API_KEY requirement, states that 'Returns exact counts', explains the currency caveat ('Harmonic funding is ~USD; EUR 30M ~= USD 32-33M'), and defines the startup/scaleup classification logic. This goes beyond typical transparency, though it omits error/limit behaviors.

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

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

The description is front-loaded with a one-sentence purpose, followed by a compact Args list and essential notes. Every sentence adds value—no fluff or repetition of schema defaults. The structure makes it easy to scan and extract key points.

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 an output schema (so return format need not be described) and no annotations, the description is complete: it covers authentication, definitions, parameter semantics, a data-quality caveat ('tags are coarser... use clean-tag segments precisely'), and the connection to the TAM sheet. It leaves no major gap for an agent to 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 description coverage is 0%, so the description must fully explain parameters. It does: each of the five parameters gets a clear description, examples (industry_tags, technology_tags, exclude_business_tags), defaults (region, split_at), and the crucial EUR/USD note for split_at. This exceeds the schema's bare types.

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 opens with a specific verb and resource: 'Count Startups and Scaleups for one value-chain segment via Harmonic, split on funding raised.' This clearly distinguishes it from siblings like get_enterprise_counts, get_segment_counts, and get_company_count, which target different count types.

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

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

The description gives clear context for when the tool is appropriate ('for one value-chain segment', 'TAM sheet columns D and E') and offers a tip on handling Harmonic's coarse tags. However, it does not explicitly compare against sibling tools or state when to use this instead of another, so the guidance is implied rather than explicit.

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