turva-mcp

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

No annotations provided, so description carries full burden. It declares 'Read-only: returns static JSON and changes nothing,' disclosing no side effects. This fully informs the agent about safety and 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, front-loaded with what it does, then usage guidance and safety note. No wasted words. 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?

For a no-parameter, no-output-schema tool, the description is fully complete. It explains purpose, data sources, and behavior. Siblings are different enough that no further differentiation is needed.

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

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

There are zero parameters, so baseline is 4. The description does not need to add parameter info. It correctly indicates no inputs are required.

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 agent-readiness scores from independent public scanners, listing specific sources (startuphub.ai, isitagentready.com) and data elements (sub-scores, leaderboard rank, notable wins, date, links). This distinguishes it from siblings like get_principles and get_services.

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 'Use this when a user asks how turva.dev scores, whether its claims are verifiable, or what proof backs the audit service.' This provides clear context and disambiguates from 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?

Description declares 'Read-only: returns static JSON and changes nothing,' fully disclosing lack of side effects. No annotations provided, but this suffices for a simple read 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?

Two efficient sentences: first states purpose and lists principles, second provides usage guidance and behavioral note. No wasted words, front-loaded.

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

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

For a parameterless tool returning static JSON, description fully explains what is returned and when to use it. No output schema needed; the description covers return nature.

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 baseline score is 4. Description adds no parameter info, which is appropriate given zero 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?

Description clearly states 'Returns turva.dev's engagement principles' with a specific verb and resource. Lists examples, distinguishing it from sibling tools like get_agent_readiness.

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 a user asks how turva.dev works with clients or what rules an engagement follows.' Lacks explicit when-not-to-use, 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?

With no annotations, the description fully covers behavior by stating 'Read-only: returns static JSON and changes nothing,' clearly indicating it is a safe, non-destructive query. It also specifies the return includes a scan date.

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, highly concise and front-loaded. The first sentence states the core function, the second provides usage context and behavioral note. Every word 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 parameters and no output schema, the description covers all essential information: what it returns (scan results from specific sources, with scan date), when to use it, and that it is read-only. It is complete for a simple query 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 tool has zero parameters, so the baseline is 4 as per guidelines. The description correctly does not add parameter information since none exist.

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 the latest public web-security scan results for turva.dev's domain, including from Hardenize and Internet.nl, with the scan date. It distinguishes from sibling tools by explicitly contrasting with 'agent-readiness scores' and focusing on security evidence.

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 explicit guidance: 'Use this when a user asks about turva.dev's own security posture or wants evidence beyond agent-readiness scores.' This provides clear context for when to use, though it could also mention when not to use (e.g., other domains).

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?

Explicitly states 'Read-only: returns static JSON and changes nothing,' fully disclosing no side effects. With no annotations provided, the description carries the full burden and meets it.

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 two-sentence description. First sentence lists contents, second gives usage and behavioral note. Front-loaded with essential information, no wasted words.

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

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

Given no output schema, the description adequately explains return values: lists services and pricing types, mentions static JSON. Could specify the exact structure but sufficient for a simple read-only catalog.

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; schema coverage 100% (empty). Description adds meaning beyond schema by detailing the returned data contents (services, pricing, engagement model). Baseline for 0 parameters is 4, and description meets it.

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

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

Clearly states the tool returns turva.dev's service catalog, listing specific services, engagement model, and pricing. Distinguishes from sibling tools (get_agent_readiness, get_principles, get_security_evidence) by being the comprehensive catalog.

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 'Use this when a user asks what turva.dev offers, what it costs, or how an engagement works.' Provides clear context for use, though does not explicitly mention when not to use, which is acceptable given the tool's simplicity.

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