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

Annotations already declare readOnly/idempotent/openWorld/non-destructive, so the safety profile is covered. The description adds real behavioral context beyond that: the Pro-plan requirement and the fact that SAM registration agents are filtered out of decision-maker results.

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 terse opener is immediately followed by a clean two-bullet breakdown of modes, so the key routing information is front-loaded and easy to scan. The resolve(company) parenthetical is slightly cryptic but earns its place as a hint.

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?

An output schema exists so return values needn't be described, and the description maps most of the six parameters plus the Pro-plan prerequisite. It doesn't state what happens if kind and supplied params disagree, a minor gap.

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

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

With 0% schema coverage, the description must carry parameter meaning, and it does: uei maps to company_decision_maker, name/email to contracting_officer, and state/agency narrow the search. It leaves format details (e.g., state abbreviation) unstated, but otherwise compensates well for the coverage gap.

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 bullet list names the two specific things the tool returns – a company's decision-maker and a contracting officer's details – which is more than the vague 'Who to talk to' opener. It clearly separates the two modes but does not differentiate itself from siblings like resolve or search beyond the resolve(company) reference.

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?

It states a prerequisite ('Both need the Pro plan') and gives per-kind input guidance: UEI for company_decision_maker (with resolve(company) to obtain one) and name/email for contracting_officer, with agency/state as narrowing filters. No explicit when-not or alternative sibling is named, so it stops short of 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?

Annotations already declare readOnly/idempotent/non-destructive, so the safety profile is covered. The description adds real value beyond them: plan-gated kinds ((Pro)) and a disclosed behavior for options.direction on organization_relationships. Missing are failure behavior for a bad ID and any note about record-not-found.

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?

Front-loaded: the core operation is the first sentence, then the ID-sourcing detail follows as a scannable per-kind list. Dense but nearly every clause carries information. Slightly hurt by the compact run-on formatting where kind lists and options notes are jammed into single lines.

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 20-way polymorphic getter with a 0%-documented schema, it covers kind meaning, ID provenance, plan gating, and per-kind options; output schema exists so return shape needn't be explained. The remaining gap is edge-case behavior (invalid ID, missing record, whether omitted ids error).

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 carries the full burden and does: it defines id semantics per kind (notice_id, PIID, UEI of 12 chars, CAGE of 5, subaward_sam_report_id, wd_id, office_code, organization_id) and enumerates the valid keys inside options for each kind that uses them. This is exactly the compensation the schema lacks.

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?

Opens with a specific verb+resource: fetch a single record by ID, and immediately distinguishes itself from siblings by stating IDs come from search results or resolve. The per-kind list makes the resource scope concrete. It stops just short of 5 because the first line alone is generic and only the follow-on list reveals what 'record' means.

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 routes the agent: IDs for each kind come from search(...) or resolve(...), and each kind is mapped to its source call. It also flags which kinds require the Pro plan. It does not state the inverse case (e.g., use search instead when you have no ID / want multiple records), which keeps it from 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?

Annotations already cover read-only, idempotent, open-world, non-destructive, so the bar is lower; the description still adds real behavior: per-code views reject filters, leaderboard requires no naics, and keywords match NAICS titles rather than topics. It doesn't discuss cost, pagination, or limits on aggregate queries, but the added constraint detail exceeds typical.

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?

Front-loaded summary sentence followed by a tight bulleted enumeration of views – no wasted prose. The compressed line-wrapping and cramming the find_codes filter spec into one bullet makes it slightly denser than ideal, but nothing is extraneous.

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 an output schema present, return values need not be described, and the annotations cover safety. The description supplies the missing pieces: view semantics, which views need naics, which accept filters, and the full filter vocabulary – everything an agent needs to call it 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 0%, so the description must carry all parameter meaning and largely does: naics is 6-digit, board takes biggest/least_crowded/most_open, and the find_codes filters keys (keywords, sector, prefix, min_market, max_competitors, set_aside_family with its five enum values, sort_by with its three values) are all spelled out. It even flags the keyword-matching gotcha ('computer', not 'cybersecurity').

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?

Starts with a specific verb and resource – 'How big a NAICS market is, who wins it, and how it buys, across every agency' – then enumerates the six views and the exact metrics each returns. It also explicitly distinguishes itself from the sibling search tool for agency-scoped queries, so an agent can route without opening the schema.

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?

Gives per-view guidance on what each view answers, states that per-code views take no filters, states that leaderboard needs no naics, and names an explicit alternative ('for one agency: search(contracts) with naics and agency'). The when-to-use and how-to-scope conditions are all present.

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

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

Annotations already declare readOnly, idempotent, openWorld, non-destructive, so the safety profile is covered. The description adds plan gating that annotations cannot express — benchmark and position require the Pro plan — which is genuinely decision-relevant. Return shapes are not described, but an output schema exists, so that burden is lifted.

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?

Front-loaded with the one-line purpose, then organized as one bullet per `kind` so an agent can jump straight to its mode. Dense but every line carries parameter or mode information. The heavy abbreviation and tight line-wrapping cost some readability without adding real 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?

For a two-parameter dispatcher over five sub-operations with an opaque filters object, the description covers mode selection, required filters per mode, enum vocabularies, plan gating, and one cross-tool prerequisite. With an output schema present, return values need not be explained. An agent has enough to call this 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 0% and `filters` is an open additionalProperties object, so the schema supplies essentially nothing; the description carries the full burden and does so well, naming filter keys and decoding enums (FFP | T&M | CPFF | CPAF | LH, 8A | SBA | SDVOSBC | WOSB | HZC | NONE, HS | AA | BA | MA | PHD, SCA | DBA, contains | exact, current | potential | obligated). It falls short of 5 because types, defaults, and whether filters are mutually compatible across modes are left implicit.

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?

States the resource (pricing/rate evidence for a bid) and then enumerates five distinct sub-operations with the specific artifact each returns (contract value distribution, rank of a value, GSA CALC ceiling rates, SCA/DBA rates, rate spread). An agent can tell what each `kind` value yields. It does not, however, distinguish this from the `market` sibling, so differentiation is incomplete.

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?

Per-mode requirements are spelled out (e.g. benchmark/position need `naics`, position needs `value` as a string, labor_rates needs `labor_category`), and it routes cross-tool for one case: "For one place, get the WD first and read its rates." That is real when-to-use guidance. What is missing is any explicit rule for choosing between `benchmark` vs `position` vs `market` when several could apply.

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

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

Annotations already declare this tool as read-only, idempotent, open-world, and non-destructive. The description adds valuable behavioral context beyond those annotations, including that NAICS matching works on titles rather than topics and that large companies may have multiple registrations requiring name/location disambiguation. It does not cover no-match or error behavior, so it falls just short of the highest score.

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

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

The description is front-loaded with the core purpose and then uses concise bullets for each resolution mode. Despite its length, every bullet explains a distinct enum mode and earns its place.

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

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

Given the two required parameters, the enum, the presence of an output schema, and rich annotations, the description supplies everything an agent needs to select the correct mode and interpret the query. It also covers ambiguous company matches and routes unsupported identifiers to the correct sibling 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%, so the description carries the full burden of explaining the parameters. It does this thoroughly by explaining what each enum value of `kind` means, how `query` should be interpreted for each kind, and what each mode returns.

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

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

The description states a specific action and scope: it turns user-mentioned names into the IDs and codes that other tools accept. It also distinguishes itself from siblings by positioning itself as the starting point before using search, get, or other tools, and it names alternatives for specific cases.

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?

It explicitly says when to start here: when the user names an agency, describes their work, or names a company. It also gives alternatives and exceptions, such as using search(companies) for ranking by award history and get(entity) for CAGE codes.

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

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

Annotations already cover read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable behavioral context beyond annotations: unknown filters return the full filter list, Pro datasets require a paid plan, result rows are large so limit should be kept small and offset used for paging, and some datasets have required filters.

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 long but necessarily so for a 17-dataset search tool. General rules are front-loaded, followed by a structured bulleted dataset reference, and each sentence provides actionable information without filler.

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 complex dataset surface and filters object, the description supplies the dataset-specific semantics needed to call the tool correctly. Annotations cover safety, and an output schema exists, so return-value details are appropriately omitted.

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 carries the full burden. It does so thoroughly by documenting the dataset enum, listing filters and required filters per dataset, explaining limit/offset usage, and noting special cases like opportunity_changes requiring since and companies requiring q.

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: 'Search one dataset,' then names every dataset available. It also distinguishes this tool from sibling resolve by explaining that agency, NAICS, and UEI values should come from resolve(agency), resolve(naics), and resolve(company).

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 per-dataset guidance: which filters each dataset accepts, which filters are required, which datasets support keywords, and when to use alternatives such as resolving identifiers through resolve or finding vehicle PIIDs through contracts/notices. This leaves little ambiguity about when and how to use the tool.

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