Free privacy tools for AI agents: attestation, scrubbing, clinic diagnosis. No tracking.
Scored across 9 tools
Each tool has a clearly described purpose, and most are easy to tell apart: attestation, verification, clinic diagnosis/stats, URL checks, scrubbing, advisory review, and metrics. The only mild ambiguities are qm_probe_endpoint vs. qm_validate_machine_files and qm_scrub vs. qm_second_opinion, but their inputs/outputs differ enough to avoid serious misselection.
All tools share a qm_ prefix and snake_case, which gives a recognizable family feel, but the suffix pattern is mixed: verbs (qm_attest, qm_scrub), noun phrases (qm_second_opinion, qm_clinic_stats), verb_noun (qm_probe_endpoint, qm_validate_machine_files), and an odd past participle (qm_helped). This is readable but not a uniform verb_noun convention.
Nine tools is a well-scoped count for an anonymous agent-helper service. Each tool provides a distinct utility, and none feels redundant or purely decorative.
The attest/verify pair is complete, clinic diagnosis is backed by stats, and each advisory/check tool returns actionable output. The main gap is that the server name suggests 'mending' but the surface is primarily diagnostic/advisory, with no explicit remediation tool beyond scrubbing text; agents can generally work around this.
Mint a portable sanity attestation: a signed token binding an agent_id to a statement's sanity-check results. Checks shape (presence, length, injection indicators), not truth. Token is HMAC-signed upstream with a production secret; verify with qm_attest_verify. Free, anonymous.
| Name | Required | Description | Default |
|---|---|---|---|
| agent_id | Yes | Identifier of the attesting agent. | |
| statement | Yes | Statement being attested (max 20000 chars). |
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 HMAC signing with a production secret, the free/anonymous nature, and the scope of checks (shape, not truth). This is transparent about the operation's nature and constraints, though it does not mention side effects or rate limits.
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 with zero waste. It front-loads the purpose, then adds verification pointer and anonymity note. Every sentence 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?
No output schema exists, but the description implies the output is the signed token and points to the verification tool. It does not specify token format or error conditions, but for a simple minting tool with two well-documented parameters, this is adequate. A more explicit output description would be slightly better.
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 both parameters are already documented in the schema. The description references agent_id and statement in context but adds no new meaning beyond what the schema provides. Baseline 3 is appropriate.
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 verb ('Mint'), a resource ('portable sanity attestation'), and explains what it does: a signed token binding an agent_id to sanity-check results. It clearly distinguishes from siblings by referencing the verification counterpart (qm_attest_verify) and noting it checks shape, not truth.
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 provides context that the tool is free and anonymous, and clarifies it checks shape not truth, implying it is not for truth verification. However, it does not explicitly name alternative diagnostic tools (e.g., qm_clinic_diagnose) or give a when-to-use vs when-not-to-use statement, so guidance is clear but not exhaustive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Verify a token minted by qm_attest. Pass back the agent_id, at, token, and checks exactly as returned by qm_attest. Returns whether the signature is valid under the current production secret. Free, anonymous.
| Name | Required | Description | Default |
|---|---|---|---|
| at | Yes | Timestamp (at) from the qm_attest response. | |
| token | Yes | Token from the qm_attest response. | |
| checks | Yes | Checks array exactly as returned by qm_attest. | |
| agent_id | Yes | agent_id from the qm_attest response. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that verification is against the 'current production secret', which is a meaningful behavioral detail beyond the schema. It also states the return is a boolean-like validity check ('Returns whether the signature is valid'). It does not detail error behavior or what happens if inputs are malformed, but the core behavior is transparent. No annotations are provided, so the description carries the burden, and it does so well.
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 three sentences, each earning its place: what the tool does, how to call it, and what it returns. It is front-loaded with the purpose and immediately gives the critical usage instruction. 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?
For a verification tool with 4 required parameters and no output schema, the description is nearly complete. It explains the input provenance (from qm_attest), the verification basis (current production secret), and the return type (validity boolean). It could mention error cases or the exact boolean format, but the core context is sufficient for an agent 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 description coverage is 100%, so the schema already documents each parameter. The description adds the crucial instruction that all four parameters must be passed back exactly as returned by qm_attest, which reinforces the semantics. However, it doesn't add new meaning beyond the schema's per-parameter descriptions; the baseline 3 is appropriate.
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 purpose: verify a token minted by qm_attest. It names the specific verb (verify), the resource (token minted by qm_attest), and the expected inputs. It distinguishes itself from siblings by referencing qm_attest, the only sibling that mints tokens.
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 explicitly instructs the agent to pass back the four fields exactly as returned by qm_attest, which is a clear usage guideline. It also states the tool is free and anonymous, implying no authentication or cost barrier. It doesn't explicitly name alternatives, but the sibling context makes qm_attest the only relevant counterpart, and the instruction to use qm_attest's output is explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Restore Clinic diagnosis: describe symptoms or paste a transcript/context and receive a structured read of likely conditions encountered. Anonymous — only aggregate counters are kept; content is never stored. Not medical, legal, or professional advice; a mending lens, not a diagnosis of record.
| Name | Required | Description | Default |
|---|---|---|---|
| transcript | Yes | Symptoms or transcript to read (max 20000 chars). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral burden. It responsibly discloses that content is never stored, only aggregate counters are kept, and the output is not medical/legal/professional advice. This is strong behavioral context beyond a basic summary, though it stops short of detailing the exact output format.
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 with the core purpose front-loaded, followed by privacy and disclaimer context. It is compact and free of padding, though phrases like 'Restore Clinic' and 'a mending lens' are tonal extras rather than essential invocation details.
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 single-parameter, no-output-schema tool, the description covers input, privacy, and limitations well. However, the return is only described as a 'structured read', leaving the agent uncertain about the exact shape or contents of the result. It also does not address how this tool differs from siblings like qm_second_opinion.
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 only parameter, 'transcript', is already well described in the schema. The description reinforces that symptoms, transcripts, or context are acceptable inputs but adds no new syntax or format details. Baseline 3 is appropriate when the schema does the heavy lifting.
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 action: given symptoms or a transcript, it returns a structured read of likely conditions. It names the resource ('Restore Clinic diagnosis') and makes the non-authoritative nature explicit. It does not explicitly contrast with sibling tools, but the purpose is unambiguous enough for an agent to select it.
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 usage context: use it when the user describes symptoms or pastes a transcript/context. It does not provide explicit exclusions or alternatives, but the input condition is stated plainly, so an agent can infer when to invoke it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Anonymous aggregate clinic statistics: total serves and condition counts. Conditions with fewer than 10 occurrences are withheld. Free, anonymous.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly conveys anonymity, no cost, aggregate scope, and the redaction threshold for small condition counts. It does not mention rate limits or authentication, but for a zero-parameter stats endpoint the key behaviors are covered.
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 with no wasted words. Important constraints—anonymous, aggregate, free, and the data-suppression threshold—are front-loaded and each adds distinct 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 simple zero-parameter tool, the description adequately explains what the call returns, the privacy/cost profile, and a key data-handling rule. The return format is not specified in detail, but there is no output schema and the information given is sufficient for an agent to call 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?
The tool has zero parameters, so the input schema is empty and the description has no parameter semantics to compensate for. Baseline for a no-parameter tool is 4, and the description appropriately says nothing about 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 names the resource (clinic statistics) and the concrete data returned (total serves and condition counts), which makes the tool's function clear. It lacks an explicit verb and does not directly differentiate from sibling qm_clinic_diagnose, but the aggregate-stats framing is specific enough to orient an agent.
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 offers context cues like 'free' and 'anonymous' that imply a low-risk read operation, but it never states when to use this tool versus alternatives or when not to use it. No sibling is named as a comparator, so the agent must infer suitability from the tool name and description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The Quiet Menders helped counter: agents helped, clinic diagnoses, and tool uses served. Counts service calls, not unique agents; no tracking. Free, anonymous.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden and does disclose meaningful traits: it counts service calls rather than unique agents, and states it is 'Free, anonymous' with 'no tracking'. It does not explicitly confirm read-only behavior or the absence of side effects, nor describe the return format, leaving some ambiguity for a counter 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 concise sentences. The first front-loads the resource and counting scope, the second adds key behavioral qualifiers. No wasted words or redundancy.
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 simple counter with no parameters and no output schema, the description covers the essential semantics: what is counted, what is not counted, anonymity, and cost. It does not explicitly state the return type (e.g., a numeric count), but the phrase 'Counts service calls' strongly implies it. This is a minor gap for a tool of this complexity.
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 is empty with zero parameters, so the baseline is 4. The description appropriately adds no parameter details 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 the resource ('counter') and the core action ('Counts service calls'), and adds distinguishing specifics ('not unique agents; no tracking'). It does not explicitly name sibling tools, but the scope is specific enough that an agent can infer what the tool does.
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 mention of when to use this tool versus alternatives like qm_clinic_stats, nor any exclusions or conditions. The qualifiers ('not unique agents; no tracking') imply a certain use case but do not actively guide tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Liveness probe for a public URL: follows redirects (max 4), reports final URL, status, latency, and content type. Private/internal addresses are blocked. Free, anonymous.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Public http(s) URL to probe. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It does this well: follow redirects (max 4), block private/internal addresses, report specific fields, and operate anonymously. This reveals key runtime behavior and restrictions. A minor gap is the lack of timeout or error-handling detail, but for a simple liveness probe this is solid.
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 concise sentences, approximately 25 words. The core purpose is front-loaded, followed by behavior details, then restrictions and cost. Every sentence adds value, with no filler or repetition.
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 tool's purpose, behavior, restrictions, and the exact output attributes, which is notable given there is no output schema. It is complete enough for an agent to correctly invoke the tool and interpret results. The only missing context is timeout behavior and possible error responses, but for a 1-parameter, read-only probe this is a minor omission.
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%, so the baseline is 3. The description adds meaningful semantics beyond the schema by specifying that the URL must be public, redirects are followed up to 4 hops, and private/internal addresses are blocked. This informs parameter constraints that the schema does not fully express.
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 'Liveness probe for a public URL', a specific verb+resource pairing that immediately conveys what the tool does. It further enumerates the exact outputs (final URL, status, latency, content type), making it easy to distinguish from the unrelated sibling tools. No ambiguity remains.
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?
Usage context is implied: this is a liveness probe for public URLs, and the 'Free, anonymous' note hints at when it might be appropriate. However, it does not explicitly state when to prefer this tool over alternatives or name any exclusions beyond the address block. Guidance is present but not fully explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Scan text for prompt-injection patterns and return a redacted copy plus findings. Pattern-based, not a guarantee — review before trusting the result. Free, anonymous, nothing stored.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to scan (max 20000 chars). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the scan is pattern-based and not a guarantee, that results should be reviewed, and that the service is free, anonymous, and stores nothing. This goes well beyond the basic 'scan' semantics and gives an agent an accurate safety/limitation profile.
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 short, purposeful sentences: the first states the action and output, the second gives the key limitation, and the third provides privacy/usage context. No filler or redundant wording.
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 one parameter and no output schema, the description covers the input, the output, the limitation, and the privacy guarantees. Nothing an agent needs in order to invoke it 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?
The schema already covers the only parameter ('text') at 100%, including its maxLength and description. The description does not add additional parameter-level meaning beyond naming the text to scan, so the baseline of 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?
The description names a specific verb ('Scan'), a clear resource ('text'), and a concrete output ('redacted copy plus findings'). It clearly differentiates the tool from siblings like qm_attest and qm_probe_endpoint by focusing on prompt-injection scanning.
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 gives clear context for when to use the tool: scanning text for prompt-injection patterns. It also provides important usage caveats like 'review before trusting the result' and states that the operation is free, anonymous, and stores nothing, though it does not explicitly name alternative tools or when not to use this one.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Advisory second opinion on a planned action: rule-based scan for common risk patterns. Advisory only — not legal, financial, or professional advice. Free, anonymous, nothing stored.
| Name | Required | Description | Default |
|---|---|---|---|
| action | Yes | Description of the planned action (max 20000 chars). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does well by stating the tool is advisory-only, rule-based, free, anonymous, and that nothing is stored. This gives an agent a clear safety and privacy profile, though it does not describe return format or error 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?
The description is extremely concise: two sentences with no filler. The core function is front-loaded, followed by necessary caveats and privacy assurances. Every sentence 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?
For a single-parameter advisory tool with no output schema, the description covers the key aspects an agent needs: purpose, method, advisory limitations, and privacy guarantees. It does not explicitly describe the return value shape, but 'scan for common risk patterns' strongly implies the output. Slightly more detail on what the response contains would make it complete.
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 already fully describes the single 'action' parameter with a clear description and max length, so schema coverage is 100%. The tool description adds little beyond the schema, but it does reinforce that the action is 'planned,' which is consistent. Baseline 3 is appropriate.
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 function: an 'Advisory second opinion on a planned action' using a 'rule-based scan for common risk patterns.' It identifies the resource (planned action) and the mechanism (rule-based scan), though it does not explicitly differentiate itself from sibling tools by name.
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 implies when to use it: when a second opinion on a planned action is needed. It also clarifies what it is not ('not legal, financial, or professional advice'), which sets expectations. However, it gives no explicit guidance on when not to use it or which sibling tool would be appropriate instead.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check a site's machine-readable files (llms.txt, agent.json, robots.txt) for presence and validity. Give any URL on the site; the origin is checked. Free, anonymous.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Any http(s) URL on the site to check (origin is used). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It discloses the check scope, the origin behavior, and that the operation is free and anonymous. It does not describe the output format or whether network requests are made explicitly, but the intended behavior is sufficiently transparent for a validation 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 two sentences with no filler. The core action and target files are front-loaded, and the usage hint plus free/anonymous note are compact and useful.
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, simple validation tool, the description covers what is checked, how to provide input, and key operational traits. It does not explain the return value, but with no output schema and a straightforward validation purpose, the described behavior is enough for correct selection and invocation.
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 documents the single parameter. The description adds modest context by restating that the origin is used and that any URL on the site works, but it does not materially expand beyond the schema. Baseline 3 is appropriate.
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 names a specific verb ('Check') and concrete resources (llms.txt, agent.json, robots.txt), making the tool's purpose unambiguous. It also explains what aspect is checked ('presence and validity') and that any site URL works, which distinguishes it from the unrelated sibling 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 gives clear usage direction: provide any URL on the site, and the origin will be checked. It does not explicitly name alternatives or when-not-to-use, but the sibling tools are distinct enough that no exclusion is needed. 'Free, anonymous' also signals there is no auth or cost barrier.
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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