Search accredited testing labs and verify lab scope evidence from ScopeMatch.
Scored across 2 tools
The two tools have mostly distinct purposes: search_labs discovers labs for a standard, while verify_lab checks evidence for a specific lab by slug or accreditation number. There is some conceptual overlap because both return scope/match-basis information, but the descriptions clarify the distinction.
Both tool names follow a clear verb_noun pattern in snake_case: search_labs and verify_lab. The convention is consistent and predictable.
Two tools is thin for a lab accreditation/scope-matching server, even if the surface is intentionally narrow. It covers discovery and verification, but feels borderline in scope.
The core read-only workflow is covered: find labs matching a standard and verify scope evidence for a specific lab. Minor gaps remain, such as browsing standards or retrieving broader lab details, but agents can likely work around them.
Find accredited labs and matching scope lines for a standard. Each result states its match basis.
| Name | Required | Description | Default |
|---|---|---|---|
| body | No | ||
| page | No | ||
| country | No | ||
| standard | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so safety is covered. The description adds genuine value by noting results include scope lines and state their match basis, but says nothing about pagination behavior despite a page parameter, nor about result volume or ranking.
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 short sentences, zero waste, front-loaded with the core action. The second sentence about match basis is compact and genuinely informative.
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 4-parameter search with 0% schema descriptions, no output schema, and a sibling tool, the description is too thin: it never explains body, country, page, or how results are paged, leaving multiple call-shaping decisions unresolvable.
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% across 4 parameters, so the description carries full burden but only covers 'standard' ('for a standard'). The body, page, and country parameters—including how body differs from standard—are entirely undocumented anywhere.
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?
Names a specific verb+resource (find accredited labs) plus the secondary return (matching scope lines) scoped to a standard. It does not explicitly distinguish itself from the sibling verify_lab, so differentiation is left implicit.
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 when-to-use or when-not guidance; the sibling verify_lab is never mentioned, so an agent must infer that search is for discovery and verify for confirmation. The only hint is the phrase 'for a standard', which implies the required scope but not the choice of tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check scope evidence by lab slug or accreditation number, not lab name. Each evidence result states its match basis.
| Name | Required | Description | Default |
|---|---|---|---|
| lab | No | ||
| body | No | ||
| standard | Yes | ||
| accreditation_number | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered structurally. The description adds that each evidence result states its match basis, which is useful behavioral context. It still does not explain what scope evidence includes, how open-world lookup behaves, or what errors may occur.
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 tightly written sentences with no wasted wording. The identifier guidance is front-loaded and immediately actionable.
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 is adequate for a read-only verification tool with annotations covering safety, but the tool has four parameters, a oneOf constraint, and no output schema. The description covers the key identifier routing but leaves standard and body unexplained, and gives only a brief hint about return content.
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% across four parameters, so the description must carry the burden. It clarifies the alternative lab and accreditation_number inputs and warns against using lab name, but it omits the required standard parameter and the body parameter entirely. It partially compensates for the schema gap but leaves too much undocumented.
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 resource: 'Check scope evidence' for a lab. It also clarifies the accepted identifiers (lab slug or accreditation number) and explicitly excludes lab name, which helps distinguish its input from a generic lab lookup. It does not name the sibling search_labs, so sibling differentiation is only implicit.
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 useful input guidance: use lab slug or accreditation number, not lab name. However, it does not explain when to use this tool versus search_labs, nor does it explain when the standard or body parameters are relevant. Usage is implied rather than fully specified.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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