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

With no annotations, the description carries the full burden. It does disclose a meaningful behavioral boundary — the output is phrase-level screening, not a validated risk-of-bias or statistical assessment — which helps an agent avoid misusing the result. It says nothing about input requirements, safety profile, or what the screening returns, leaving substantial behavioral gaps.

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 single-sentence fragment is brief but under-specified rather than concise — it spends its only content on exclusions and never describes the action. Front-loading the scope limitation is good, but the definition is too thin to earn 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?

In a dense sibling set with multiple audit and statistical-checking tools, no annotations, no output schema, and 0% parameter documentation, the description should do far more. It usefully carves out a niche versus audits but omits return format, expected input content, and any usage conditions.

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% for both parameters, so the description must compensate and does not. It offers no indication of what source_text should contain (abstract? full paper? methods section?) or what source_id refers to, despite source_text format being critical for a screening tool.

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 identifies the operation as 'transparent phrase screening,' which gives some sense of scope and distinguishes it from heavier siblings like audit_methodology and check_statistical_reporting. However, it never states a concrete verb-plus-resource ('screens methodology phrases in a source text') or what the screening produces, so the purpose is only implied.

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 'NOT a RoB-2, GRADE, or statistical audit' clause effectively signals when this tool is the wrong choice and implicitly routes the agent toward audit_methodology/check_statistical_reporting. It never states when this tool IS the right choice or names those alternatives explicitly, so guidance is only half-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?

With no annotations, the description carries the full behavioral burden and does disclose meaningful limits: it verifies quote-to-text traceability and explicitly does not scientifically validate labels. However, it omits other behavioral facts an agent needs, such as whether the operation is read-only, what it returns, and how mismatched quotes are reported.

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, front-loaded statements with no filler; the core purpose leads and the caveats follow. The fragmented line breaks are mildly awkward but do not impede comprehension.

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 five required parameters, an untyped nested annotations array, no output schema, and no annotations, the description is under-specified. While the scope-limiting caveats are useful, an agent still lacks parameter semantics and any sense of the return shape needed 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 0% across five required parameters, so the description must compensate but largely does not. It loosely maps to a few fields ('outcome-specific', 'user-annotated', 'supplied raw text') but gives no meaning for study_id or study_design and no format for the annotations array, leaving most parameters 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?

States a specific verb (audit) and resource (traceability of methods evidence, with quotes checked against raw text), so the agent knows this is a traceability-verification tool rather than a validity assessor. It does not, however, distinguish itself explicitly from close siblings like audit_pmc_methodology or assess_methodology, leaving that separation to inference.

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 carves out a clear boundary ('Quotes are checked against supplied raw text', 'Labels are never scientifically validated'), which implicitly tells the agent this is a limited scope-check rather than a full appraisal. But it never states when to choose this over audit_pmc_methodology or assess_methodology, nor any prerequisites, so usage is only implied.

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 carries the full behavioral burden. It usefully discloses that it pulls external Europe PMC XML and that no scientific conclusion, risk-of-bias judgement, or absence claim is inferred, which meaningfully bounds interpretation of results. It still omits auth requirements, failure behavior when a PMC ID is invalid, and what the validation result contains.

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, front-loaded with the action before the limitation. The second sentence is a slightly awkward fragment, but nothing is padded or wasted.

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?

A tool taking four required, undocumented arguments with no output schema and no annotations needs more than two sentences. The description never explains the annotation format, what 'outcome' and 'study_design' should contain, or how validation findings are returned.

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?

All four parameters are required with 0% schema description coverage, and the description mentions none of them. The non-obvious 'annotations' array of free-form objects and the 'outcome'/'study_design' strings are left entirely unexplained in both the schema and the prose.

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 two specific actions (fetch Europe PMC XML, validate user annotations) against a concrete resource (direct JATS paragraphs), which is far more informative than a bare 'audit' label. It does not, however, distinguish this from the sibling 'audit_methodology', so an agent cannot tell the two apart from the text alone.

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?

There is no statement of when to use this tool versus the similarly named audit_methodology, no prerequisites, and no conditions that select it over alternatives. The only scoping present is a negative claim about what is not inferred, which bounds output rather than guiding selection.

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?

No annotations are provided, so the description carries the full burden. It does not say whether validation persists anything, what a failure looks like, what makes a graph valid, or what 'traceable evidence identifiers' must conform to. The name/description tension between 'build' and 'validate' further obscures whether this is a read-only check or a mutation.

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?

A single front-loaded sentence with no padding, so it is structurally efficient. The problem is under-specification rather than verbosity, which caps the score at adequate.

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?

A graph-building/validation tool with no annotations, no output schema, and zero parameter documentation needs a far richer description. Nothing tells the agent the expected graph shape, the validation criteria, or what is returned.

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% and both parameters are unconstrained arrays of arbitrary objects (additionalProperties: true). The phrase 'traceable evidence identifiers' is the only hint at expected node/edge content, and it is far too vague to compensate for the missing schema documentation of two required 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 states a specific verb and resource: validate an evidence graph with traceable evidence identifiers. However, the tool NAME says 'build' while the description says 'validate' — an agent cannot tell whether this constructs or only checks a graph. It also does nothing to distinguish itself from close siblings like verify_evidence_ledger or export_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?

No when-to-use guidance, no prerequisites, and no mention of alternatives. With siblings such as verify_evidence_ledger and verify_claim in the same family, the agent is given no basis for choosing this tool over them.

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 carries the full behavioral burden, and it does disclose one important trait: the two upstream data sources consulted and the fact that a null result cannot be treated as confirmation of validity. It says nothing about authentication, rate limits, latency, or how a hit is reported.

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?

One tight sentence with no filler, and the caveat is placed where it will be read. It is dense and jargon-heavy ('update-to'), but every clause 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?

There is no output schema, so the description should ideally describe what a positive hit looks like; it only frames the negative case. The absence-inconclusive note is valuable, but return-shape and source-precedence details are missing for a tool whose whole value is its answer.

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% and the single 'doi' parameter is never mentioned in the description, so it adds no syntax or format guidance (e.g., bare DOI vs. URL form). The parameter name is self-explanatory enough that this is a moderate rather than severe 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?

States a specific verb (Inspect) and a well-defined resource (Crossref update-to and Retraction Watch-linked updates), which is enough to separate it from siblings like verify_citation or get_paper. It stops short of naming the input (a DOI) or the output, so it is clear but not maximally so.

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 clause 'absence is inconclusive' is a genuine interpretive caveat that tells the agent how to read a negative result, which implies when this tool is worth calling. However, it never states when to prefer this over verify_citation or get_paper, nor any prerequisite or sequencing guidance.

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?

No annotations are provided, so the description carries the full behavioral burden. It implies a read-only detection pass but never states whether anything is mutated, what permissions are needed, what the output looks like (no output schema exists), or how many records it can handle — none of the behavioral traits beyond the one-sentence scope are disclosed.

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?

A single front-loaded sentence with the core scope stated first and the exclusion tacked on efficiently. Every clause earns its place; nothing is padded.

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 this is a thin but workable definition. The hint about which statistical quantities matter partially compensates for the completely opaque record schema, but with no annotations and no output schema, the agent still lacks enough to construct correct input records with confidence.

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% with a single untyped array-of-objects parameter (additionalProperties: true, no defined keys), so the description must explain what a "record" must contain. It hints at the relevant fields (p values, CIs, sample sizes) but never states the expected key names, required vs optional entries, or the record structure an agent must build.

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 ("Find") and a precisely bounded resource ("mechanically inconsistent p values/CIs/sample-size entries"), then distinguishes itself from broader siblings like assess_methodology by excluding semantic judgment ("not test results"). An agent can tell this apart from the other audit tools 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?

The phrase "not test results" gives an explicit when-not boundary, steering the agent away from using this for interpreting or assessing findings. However, it names no concrete alternative sibling to route to when the task actually is about test results, so guidance is clear but not fully actionable.

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 carries the full behavioral burden and largely fails: it does not say how comparison is performed, whether the operation is read-only, what permissions or input validity are required, or what the result looks like. The only disclosure is a negative scope constraint (no inferred theoretical disagreements), which is useful but thin.

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?

One short, front-loaded sentence with no filler, so there is nothing to trim. However, the brevity is closer to under-specification than to disciplined conciseness for a tool with an opaque nested input.

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 one structurally undefined parameter, no output schema, and no annotations, the description should explain expected input shape and return behavior. It explains neither, leaving the agent unable to construct 'papers' confidently or predict results.

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 single parameter 'papers' is an array of free-form objects with additionalProperties=true and 0% schema description coverage. 'Supplied bibliographic metadata' hints at expected content but gives no field names, shapes, or required keys, so the agent must guess the object structure entirely.

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 and resource ('Compare supplied bibliographic metadata'), which is clearly distinguishable from siblings like audit_methodology or check_statistical_reporting. The scope caveat ('no inferred theoretical disagreements') further narrows intent, though no sibling is named explicitly.

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 'no inferred theoretical disagreements' clause implicitly bounds what the tool is for, but there is no when-to-use condition, no prerequisite, and no named alternative among the 16 siblings. An agent gets a limitation without a selection rule.

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 must carry behavioral weight. It discloses that the operation appends to a local hash-chained JSONL ledger, which implies durable, tamper-evident, local writes. However, it omits error behavior, idempotency, permission requirements, and whether an existing chain is required.

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?

It is a single front-loaded sentence with no filler. While very short for the tool's complexity, the sentence itself earns its place and is not padded.

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 tool has two undocumented required parameters, a nested payload object, no annotations, no output schema, and 0% schema description coverage. A single-sentence description is not enough for an agent to call this 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?

Schema description coverage is 0% for both required parameters. The description never explains what 'kind' should contain or what shape the 'payload' object takes, leaving the agent with no semantic guidance for either argument.

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 ('Append') and resource ('JSON evidence record to local hash-chained JSONL ledger'), which is more specific than a generic 'export'. It does not explicitly differentiate from siblings like verify_evidence_ledger or build_evidence_graph, so it stops short of a 5.

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?

There is no when-to-use guidance, no prerequisites, and no mention of alternative tools. The agent is left to infer that this is the write-side counterpart to verify_evidence_ledger.

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 carries the full burden. It usefully discloses that results are conditional on registration and that it is a lookup-only operation that does not retrieve the underlying artifacts, but it omits auth requirements, failure behavior, and return shape.

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?

One tight sentence with the core action front-loaded and the negative constraint appended. Nothing is wasted.

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 one-parameter lookup with no output schema or annotations, the description covers the essentials: what it finds and that it will not fetch. It leaves the return format and the not-registered case underexplained, which matters since there is no output schema to fall back on.

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 the single required parameter (doi) is never mentioned in the description. The name is fairly self-evident, but the description adds no meaning such as accepted DOI formats or whether prefix-only DOIs work, so it fails to compensate 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 description states a specific verb and resource: find Crossref links to supplements, data, or software. It clearly distinguishes itself from content-retrieval siblings like read_fulltext and get_paper by scoping to link discovery, though it never names a sibling explicitly.

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?

"If registered" and "do not fetch them" imply when the tool is useful (locating links only, not retrieving content) and set an expectation of possible empty results. However, no alternative tool is named for the actual fetching step, so routing is left to inference.

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?

No annotations are provided, so the description carries the full burden. It discloses the data source (Crossref) and the key limitation (no conclusion validation), which is real value, but says nothing about authentication, rate limits, or behavior on an unresolvable DOI. Adequate but incomplete for a no-annotation 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?

A single sentence with the scope claim front-loaded and the limitation attached via a semicolon. Every clause earns its place; 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 lookup with no output schema, the description supplies the data source, the retrieval target (metadata and source links), and the explicit non-goal. What is missing, return shape and failure behavior for bad DOIs, is modest given the tool's simplicity.

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% and there is one parameter ('doi'), so the description must compensate. Calling out 'Crossref DOI' implies the identifier namespace, which is useful, but it adds no format example, prefix guidance, or resolution behavior. Partial compensation only.

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 and resource: 'Get Crossref DOI metadata and source links.' The negative clause 'does NOT validate scientific conclusions' further separates it from validation-oriented siblings such as verify_citation and verify_claim. However, it doesn't explicitly declare which sibling to use for validation, so it stops short of a 5.

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 negation ('does NOT validate scientific conclusions') implicitly signals when-not-to-use, which is useful routing against verify_claim/verify_citation. But there is no positive guidance on when to reach for this tool (e.g., 'use this to fetch bibliographic metadata before citing') and no named alternative for the validation case.

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?

No annotations are provided, so the description carries the full disclosure burden. Beyond the phrase 'reproducible search records', it says nothing about what gets logged, where records are stored, whether the operation mutates state, required permissions, or what the response contains. For a search-plus-persist tool this is a significant gap.

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?

A single tight sentence with the core action front-loaded and the clarifying exclusion appended. It wastes no words, though the extreme brevity borders on under-specification rather than genuine conciseness.

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 no annotations, no output schema, and 0% parameter documentation, the description is the only source of behavioral detail and provides almost none. An agent cannot determine what the tool logs, what it returns, or how it differs operationally from the many search/verification siblings.

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% for both parameters. The description does not explain what 'query' accepts or how 'limit_per_source' behaves (its unit, default, or per-source meaning). With two undocumented parameters, the description needed to compensate and does not.

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 specific verbs and a resource: 'search + log reproducible search records'. The 'NOT an exhaustive systematic review' clause hints at scope, differentiating it conceptually from a full-review tool. However, it never names or distinguishes itself from the closest sibling, search_literature, so an agent still has to guess which search tool fits.

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 'NOT an exhaustive systematic review' boundary gives a when-not signal, implying this is a lightweight, reproducible-search-logging step. But there is no explicit when-to-use guidance and no named alternative (e.g., search_literature) to route between, so the agent must infer selection.

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 carries the full burden, and it does disclose that the response includes a source hash and a truncation flag, plus the OA-only limitation. It omits failure behavior for non-OA items, rate limits, and what happens when truncation occurs, so disclosure is partial.

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?

A single tight sentence with the operation and source front-loaded and no filler; every clause conveys information.

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 no annotations, no output schema, and 0% schema coverage, the description gives only a partial picture: it names the return elements but leaves error handling, non-OA behavior, and max_chars semantics unexplained.

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 should compensate. Mentioning 'truncation flag' loosely connects to max_chars (default 30000), but neither parameter's format, default, or effect is explained beyond that hint.

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 gives a specific verb ('Read'), resource ('OA full-text XML'), identifier ('by PMCID'), and source ('Europe PMC'), so an agent knows exactly what it retrieves. It does not explicitly distinguish itself from siblings like get_paper or verify_source_quote, which is the only gap.

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 'OA' qualifier implies this only works for open-access articles, which is a useful implicit constraint, but there is no explicit when-to-use guidance or comparison against get_paper (metadata) or search_literature. Usage context is inferable rather than stated.

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 carries the full disclosure burden. It earns credit for two non-obvious behaviors — cross-provider DOI deduplication and reporting each provider error — but omits whether the call is read-only, rate/limit behavior, authentication needs, and result shape or pagination.

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?

A single front-loaded sentence with no filler: verb first, then providers, then dedup/error behaviors. It is slightly telegraphic ('report each provider error' leaves format unspecified), but structure and size are appropriate.

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 multi-provider search tool with no annotations, no output schema, and 0% schema coverage, the description is materially incomplete. It omits query syntax, return format, pagination/limit semantics, and error/dedup output shape, all of which an agent needs before invoking it.

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% for both parameters, so the description must compensate and does not. It says nothing about query syntax (boolean? DOI? identifiers?) or what limit_per_source controls, leaving the agent to guess both parameter meanings.

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?

Specific verb ('Search') plus three named resource providers (OpenAlex, Europe PMC, Crossref), with two extra behaviors (DOI dedup, per-provider error reporting). It is clearly distinguishable from verification/audit siblings by subject matter, but it never contrasts itself with the closest neighbor, find_data_and_code, which also performs scholarly discovery.

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 statement of when to use this versus alternatives such as get_paper, read_fulltext, or find_data_and_code, and no prerequisites or exclusions. Usage is only inferable from the verb 'Search', which is not enough guidance when the sibling set overlaps on discovery.

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 carries the full burden. It usefully discloses that the check is against Crossref and that it is a bibliographic match rather than a publication-validity judgment, but it does not describe return behavior, match strictness, failure modes, or external API constraints.

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 a single, front-loaded sentence with no wasted words. The scope and key exclusion are communicated immediately.

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?

There is no output schema and no annotations, so the description should do more to explain what a successful or failed verification returns. It gives the core scope but leaves important agent-facing details absent.

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 must compensate for both required parameters. It mentions 'DOI/title match,' which ties the parameters together, but it does not explain DOI formatting, title-matching behavior, exactness requirements, or how the claimed title is compared.

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 ('Verify') and resource ('bibliographic DOI/title match in Crossref'), and the parenthetical distinguishes it from publication-validity checks. It does not explicitly name sibling tools, but its scope is clear enough to separate it from claim or source verification.

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 implies usage by saying it verifies a DOI/title match and explicitly excludes checking the validity of the publication. However, it does not say when to use this instead of alternatives like verify_claim, verify_source_quote, or check_retraction.

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 carries the full burden and does disclose one important limitation: it is a literal-string check, not scientific verification. It still omits what a pass/fail result means, whether it is read-only, and failure behavior, so it is only partially transparent.

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, both earning their place, with the core function front-loaded and the caveat immediately after. No padding 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?

With no annotations, no output schema, and three entirely undocumented parameters, the description does not supply enough for an agent to call the tool correctly or interpret its result. The limitation caveat helps but leaves substantial gaps.

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 three required parameters, and the description says nothing about quote, source_text, or source_id. It therefore fails to compensate for the missing schema documentation, leaving an agent to infer the relationship between quote and source_text.

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 object ('Check literal quote presence'), which is clearer than the tool name 'verify_claim' suggests. However, it does nothing to distinguish itself from the near-identical sibling 'verify_source_quote', so an agent cannot tell which to select without opening both schemas.

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 second sentence functions as a when-not guideline ('This cannot scientifically verify a claim'), steering the agent away from treating it as scientific validation. But there is no positive when-to-use statement and no explicit routing to an alternative such as verify_source_quote or assess_methodology.

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 carries the full behavioral burden. It usefully discloses a key limitation (it cannot detect whole-ledger replacement), which is genuinely valuable scope information, but it omits what checks are performed, whether the operation is read-only, and what a pass/fail result looks like.

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?

A single clause states the action first, then the critical scope limitation, with zero filler. Every word earns its place and the most decision-relevant caveat is front-loaded after the core purpose.

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 zero-parameter tool with no annotations and no output schema, the description covers purpose and one limitation but does not describe the return value or what 'integrity' encompasses. Adequate but leaves an agent guessing at the outcome semantics of a verification call.

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 takes zero parameters, so per the baseline this scores 4. There are no input semantics for the description to clarify, and the empty schema is consistent with the description's self-contained verification scope.

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 and resource ('Check local JSONL ledger integrity'), and the 'ledger' resource clearly differentiates it from sibling verify_* tools like verify_citation and verify_claim. However it never names or contrasts with any sibling explicitly, so the differentiation is inferred from the unique resource rather than stated.

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 its usage context (verifying a local ledger's integrity) but gives no explicit when-to-use trigger and names no alternative tools. The 'cannot detect replacement of the entire ledger' clause is a scope caveat rather than routing guidance, so an agent has only an implicit sense of when to prefer this over other verification tools.

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?

No annotations are provided, so the description carries the full burden. It discloses retrieval of external full text but says nothing about failure behavior (quote not found), whether the pmcid must be an exact PMC identifier, rate limits, or what the result looks like. 'NOT a scientific verdict' is a useful output-scoping caveat but far from complete behavioral disclosure.

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?

A single compact sentence with the action front-loaded and the important limitation trailing. No filler, though the semicolon-heavy phrasing is slightly terse.

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-required-parameter tool with no annotations and no output schema, the description should explain return shape (found/not-found, position, matched text) and any pmcid format expectations. It omits both, leaving the agent unable to interpret results.

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 neither parameter has a description, so the description is the only source of meaning. It implies 'pmcid' identifies the Europe PMC source and 'quote' is the string being located, which covers both params conceptually, but adds no format, matching, or normalization guidance.

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 chain (Retrieve + locate) on a specific resource (Europe PMC full text / source paragraphs) and the boundary condition 'NOT a scientific verdict', which distinguishes it from siblings like verify_claim and verify_evidence_ledger. Clear without needing to inspect 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?

The 'NOT a scientific verdict' clause implies when NOT to rely on this tool's output, but no alternative sibling is named and no positive trigger condition (e.g., 'use when you have a pmcid and an exact quote string') is given. Usage is only implied.

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