Menso

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

Goes well beyond the readOnly/openWorld annotations by disclosing plan-tier behavior differences (Studio vs Free/Pro output), that returned text originates from the tested site, that it is wrapped in <site-content> tags, and a direct instruction to treat it as untrusted evidence rather than commands. This is exactly the kind of prompt-injection and data-provenance context annotations cannot carry.

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-loads what is returned before the plan-tier and safety caveats, and every clause is substantive. The single long sentence is dense but not padded; it could be split for readability, which is the only deduction.

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?

Output schema exists so return values need no further explanation, yet the description still clarifies output structure and plan gating, and it fully covers the one input. Nothing an agent needs to call this correctly is missing.

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% and the single test_id parameter is fully documented (format, source from run_test), so the schema already carries the semantics. The description adds no format or sourcing detail beyond what the schema states, matching the baseline 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?

States a specific verb (get) and resource (results of a finished Menso test) and enumerates what is returned: TRACES score, per-dimension scores and reasons, task outcome, and plan-dependent friction points. This clearly separates it from siblings like run_test and get_status.

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 phrase 'results of a finished Menso test' implies this is only for completed tests, routing the agent to get_status for in-progress checks. However, it never names an alternative tool or states the precondition explicitly, so the guidance is contextual rather than spelled out.

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 (readOnlyHint=false, idempotentHint=true, openWorldHint=true) are reinforced rather than repeated: the description explains that calling again returns the existing link, that the result is publicly accessible to anyone, and that Free-plan calls return an upgrade note instead of a link. These are material operational facts not derivable from the structured fields.

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?

Three sentences, front-loaded with the action and artifact, then the sharing risk, then the plan constraint. Every sentence carries distinct, actionable information with no 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?

For a one-parameter tool with an output schema, the description covers everything the agent needs: the precondition (finished test), the side-effect profile, the public visibility, and the plan-dependent outcome. Return-value formatting is correctly left to the output schema.

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% and test_id is fully documented there, including the pattern and provenance from run_test. The description adds no syntax or format detail beyond what the schema provides, so the baseline 3 applies.

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 a specific verb+resource pair ('Create, or return the existing, public replay link for a finished Menso test') and immediately clarifies what the artifact is (a menso.io/r/... page showing steps and thoughts). An agent can distinguish this from get_findings/get_status/run_test without opening any 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?

Clearly scopes usage to a 'finished' test and warns that sharing is deliberate because anyone with the link can view it, plus notes plan gating (Pro/Studio vs Free). It gives conditions but does not name a sibling alternative or expand on when *not* to request a link.

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 the safety profile (readOnlyHint=true, openWorldHint=false), and the description adds far more: the full state machine, the meaning of each state, the human-in-the-loop pause condition, and the recommended polling cadence. This is exactly the kind of behavioral context that cannot be inferred from structured fields.

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?

Purpose and prerequisite are front-loaded, followed by the state enumeration that gives each state an interpreting gloss, and it closes with the polling instruction. No sentence is filler despite the density.

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 need not be re-explained; the description instead supplies the interpretation of each state and the next action for terminal states. Nothing an agent needs to call and act on this tool is missing.

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% and the single test_id parameter is already documented as 'returned by run_test'. The description's mention of run_test adds no syntax or format detail beyond the schema, so the baseline 3 applies.

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 a specific verb+resource ('Check a Menso test') and ties it back to its sibling run_test, which produced the test_id. An agent can immediately tell this is the polling companion to run_test rather than get_findings or get_replay_link.

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 to poll every 30-60 seconds and routes the agent to get_findings once state is 'done'. It also flags that 'paused' requires human action in the Menso web app, which is an actionable when-to-act condition.

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 only give the generic flags (readOnly=false, idempotent=false, openWorld=true); the description adds the details an agent actually needs: credit cost per tier (40/300), that the balance is checked before anything starts, that runs take several minutes and must be polled, that the URL must be publicly reachable, and that the signup template creates a real account on the target site with the supplied credentials.

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?

Dense but front-loaded: purpose first, then templates, then cost, then constraints, then the polling handoff. Every sentence earns its place except the template glosses, which partially restate the enum descriptions already present in the schema.

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 mutation-style, credit-consuming, multi-minute operation this covers cost, balance checking, environment requirements, side effects and the follow-up call sequence. An output schema exists, so the description correctly does not spend words on return values.

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 schema already carries the baseline. The description still adds cross-parameter meaning the schema does not: that signup consumes the email and password 'you pass' in order to create the account and land on the signed-in home, and that purchase is the default that short-circuits the need for credentials.

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 first sentence states a specific verb and resource: 'Start a Menso test on a live website.' It goes further by defining what the test actually is (an AI persona on a real cloud browser attempting a task) and what the output is (a TRACES score plus a step replay), which cleanly separates it from the sibling polling tools get_status, get_findings and get_replay_link.

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 when-to-use guidance for both templates ('purchase' needs no account; 'signup' requires email/password and continues to the signed-in home), plus explicit routing to alternatives after starting: 'poll get_status every 30-60 seconds, then call get_findings.' It also states an environment precondition (public URL, use a preview deployment, not localhost).

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