peptides.farm

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

Annotations already declare readOnlyHint and openWorldHint, so safety is covered. The description adds important behavioral context beyond annotations: redistribution requires a commercial license and access is paid via x402 USDC or credits with an X-API-KEY. It does not describe output size or rate limits, but the added licensing/payment detail is substantial.

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. The core output is front-loaded, followed by the licensing and payment conditions.

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 parameters and no output schema, the description supplies the key missing context: the result is one JSON document containing the entire corpus, and access has commercial-license and payment requirements. It could say more about expected document size or error behavior, but it is sufficient for correct 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?

There are zero parameters, so parameter semantics baseline is 4. The description correctly does not spend space explaining inputs, and no parameter meaning is missing.

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 the scope precisely: 'Entire corpus as one JSON document.' It is distinguishable from sibling tools like search or get_peptide because it returns the full corpus in bulk. The implicit operation is clear from the name and scope, though it never explicitly uses 'export' as a verb.

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 full-corpus scope implies when the tool is appropriate versus individual getters/search, but no explicit when-to-use or when-not-to-use guidance is provided. It also mentions licensing and payment prerequisites, which is useful context but not alternative routing.

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 declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds genuinely useful context beyond that — free daily allowance, then $0.05 USDC via x402 or 3 credits with X-API-KEY — which an agent needs for cost/rate awareness. It does not disclose what the snapshot contains or how many calls consume allowance.

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 compact fragments with the core purpose front-loaded and zero filler. The pricing parenthetical is dense but earns its place as cost information; nothing is redundant.

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 read-only tool with no output schema, the definition covers purpose, cardinality limits, and billing. The only remaining gap is which attributes the comparison returns, which is a minor omission for a snapshot tool.

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

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

Schema coverage is 100% and the single parameter (comma-separated slugs) is already documented. The description adds a real constraint the schema omits: the count must be 2-5 peptides, which the schema does not enforce or state.

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?

"Side-by-side snapshot of 2-5 peptides" states the resource (peptides) and the operation (comparison snapshot), which distinguishes it from single-record siblings like get_peptide or get_dossier. It never names an alternative explicitly, so sibling differentiation is implied 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?

There is no guidance on when to use this versus get_peptide, get_peptide_summary, or get_dossier for multi-peptide questions. The only conditional content is the billing note (free allowance, then paid), which tells the agent about cost, not about tool 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?

Annotations already cover the safety profile (readOnlyHint=true, openWorldHint=false), so the bar is lower, and the description still adds real context: the counts are spontaneous reports, not incidence — a genuine interpretive caveat that prevents misuse. It also discloses the auth/billing model (free daily allowance, then $0.05 USDC via x402 or 3 credits with X-API-KEY), which matters for invocation planning. It stops short of noting recency or reporting-bias 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?

Two sentences, front-loaded with the data source and return shape, then the caveat, then the billing parenthetical. The pricing clause is boilerplate but genuinely actionable, so little is wasted; the payload is dense and well ordered.

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 output schema, the description carries the return-shape burden and does so: total counts, serious counts, and top MedDRA reactions with counts. Since only one fully-documented parameter exists and safety annotations are present, remaining gaps are minor — how many 'top' reactions are returned and whether a time window applies are unstated.

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% for the single 'slug' parameter, so the schema already carries the semantics. The description adds nothing about the slug (format, where to obtain it), leaving it at the baseline for a fully-documented single parameter.

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 and data source: 'FDA FAERS adverse-event signal' returning total and serious report counts plus top MedDRA reactions. It is clear what an agent gets back, but it never separates itself from the sibling get_safety, which is a plausible alternative for the same underlying need.

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 is only implied by the data source ('FDA FAERS adverse-event signal') — an agent infers this is the safety-signal lookup rather than a trial or regulatory lookup. There is no explicit when-to-use, no exclusion, and no mention of when get_safety or get_approvals would be the better pick.

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 establish readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds genuinely non-structured behavior: a free daily quota followed by a paywall ($0.02 USDC via x402, or 1 credit via X-API-KEY), which materially changes how an agent should budget calls. It stops short of describing empty-result or error behavior, keeping it out of the top band.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences: the returned data is front-loaded, and the billing caveat is parked in parentheses where it doesn't obstruct the core purpose. The field enumeration is dense but each item is substantive rather than 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?

With no output schema, the description usefully doubles as a return-value summary by listing the data families returned. Combined with full parameter coverage and explicit annotations, an agent has enough to call it correctly; only the sibling-selection guidance 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?

Only one parameter and schema coverage is 100%, with 'slug' documented as 'Peptide slug', so the schema already carries the meaning. The description never mentions the slug input, but it doesn't need to; baseline 3 applies when structured data does the work.

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 resource ('approval timeline') and enumerates the exact data sources returned (Drugs@FDA applications, DailyMed label set IDs, ChEMBL max phase, first-approval year, mechanism of action). This is far more concrete than a tautology. It doesn't explicitly contrast itself with close siblings like get_regulatory or get_dossier, so an agent still has to infer which regulatory-flavored tool to pick.

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 get_regulatory, get_dossier, get_safety, or get_peptide_summary, all of which live in the same regulatory/approval neighborhood. The only conditional guidance is about billing (free daily allowance, then paid), which tells the agent nothing about tool 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?

Annotations already establish readOnlyHint=true and openWorldHint=false, so the description's real contribution is the commercial model: a free daily allowance, then either $0.02 USDC via x402 or 1 credit with an X-API-KEY. That is genuinely useful pre-call context an agent cannot get from structured fields, though it says nothing about behavior when a peptide has no citations.

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 compact clauses with no filler; the core purpose leads and the billing parenthetical follows. The pricing clause is dense but every element earns its place by warning of cost.

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 read-only lookup with no output schema this is close to sufficient, but it never indicates what a citation record contains (identifiers, source names, links) or what an empty result looks like. An agent can call it correctly but cannot predict the payload.

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 slug parameter is documented as 'Peptide slug', so the schema carries the load. The description's phrase 'for a peptide' confirms the mapping but adds no format, resolution, or fallback detail beyond it.

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

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

Names a specific resource (source citations) bound to a specific entity (a peptide), which separates it from siblings like get_identifiers, get_dossier, or get_safety. It is a noun phrase rather than a verb+resource statement, but the intent is unambiguous.

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 guidance on when to prefer this over adjacent tools such as get_dossier or get_identifiers, and no statement of when citations are worth fetching versus other evidence tools. The only contextual detail given is billing, not selection logic.

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?

Goes beyond the readOnlyHint/openWorldHint annotations by disclosing the commercial model: a free daily allowance, then $0.05 USDC via x402 or 3 credits with X-API-KEY. That cost/auth disclosure is exactly the kind of context an agent needs before invoking, though it says nothing about response size or latency for a multi-section payload.

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 payload contents front-loaded and the cost/auth caveat trailing in parentheses. Dense but every clause carries information; the enumeration is long yet purposeful.

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 composite read tool with no output schema, enumerating the returned sections is the right substitute for documenting return values, and the billing model is covered. Remaining gap is guidance on when the aggregate is preferable to the cheaper single-purpose 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?

Only one parameter, fully described in the schema (coverage 100%). The description's 'for one peptide' confirms the slug identifies a single peptide rather than a list, but adds no format, casing, or example detail beyond the schema.

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 resource (one peptide's dossier) and enumerates its contents — snapshot, crosswalk, approvals, trials, FAERS, literature, citations, studies, safety — so an agent knows exactly what it returns. It doesn't explicitly contrast itself with the many single-purpose siblings (get_approvals, get_trials, get_safety...), leaving that differentiation implicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'in one call' implies this is the aggregator to prefer over fanning out to individual sibling tools, but the description never states that preference or names any alternative/when-not condition. Pricing text implies repeated calls should be rationed against the free daily allowance, which is weak indirect 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?

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds meaningful context beyond that: a free daily allowance, then per-call pricing ($0.02 USDC via x402 or 1 credit with X-API-KEY), and that the UNII value is cross-checked against openFDA — all genuine behavioral/auth/cost 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?

One compact sentence with the returned-field list front-loaded and pricing moved to a parenthetical. Dense but every element earns its place; the identifier list is long but informative rather than 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?

With no output schema, the description usefully enumerates what comes back (UNII, CAS, PubChem CID, InChIKey, formula, ChEMBL ID, INN, ATC codes, source URLs). The main gap is that it never states the input is a peptide slug or what happens when an identifier is unavailable.

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?

Only one parameter (slug) and schema description coverage is 100%, so the schema already documents it. The description never mentions the slug or explains what a valid peptide slug looks like, so it adds nothing beyond the schema. 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?

The phrase 'Identifier crosswalk' plus an enumeration of identifier types makes clear what data is returned, but there is no explicit verb ('returns', 'looks up') and no statement of the input scope (a peptide slug). An agent can infer this returns cross-referenced identifiers for a peptide, but it must read the schema to know what it operates on.

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 guidance on when to use this instead of siblings like get_peptide, get_dossier, or get_regulatory, and no exclusions or prerequisites stated. The only context is the pricing/allowance note, which is about cost, not 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?

Annotations cover readOnlyHint and openWorldHint, and the description adds genuinely non-obvious behavior: a free daily allowance followed by metered cost ($0.02 USDC via x402 or 1 credit with X-API-KEY). That pricing/quota disclosure is real value an agent cannot get from annotations. It doesn't mention rate limits or what a lookup miss returns, keeping it below 5.

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 for purpose plus a parenthetical for billing; front-loaded and no filler. Minor deduction because the two concerns are jammed into a single sentence rather than separated.

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 read-only single-param lookup is a simple case, and the billing detail is a helpful addition, but with no output schema the description should say more about what the snapshot returns (depth, format) and what happens for unknown slugs or when the free allowance is exhausted.

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 param ('slug' or alias) is already described in the schema. The description adds nothing about slug formatting or alias resolution beyond that, so 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 (implicit fetch) and resource (peptide snapshot) and enumerates the returned sections: identifiers, mechanism, indications, at-a-glance. It distinguishes itself from siblings like get_identifiers or get_peptide_summary by being the composite 'full snapshot', though it doesn't explicitly say 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 word 'Full' combined with sibling names such as get_peptide_summary and get_identifiers implies this is the broad composite call, but there is no explicit when-to-use/when-not guidance or named alternative for narrower needs.

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 declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds the return payload shape and the '(free)' cost signal, which is genuinely useful behavioral context not present in structured fields, but it says nothing about rate limits, caching, or what happens when a slug/alias doesn't resolve.

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 sentence plus a cost tag, front-loaded with the 'answer-first' framing and the payload contents — nothing redundant. It is slightly telegraphic and the '(free)' tag is left unexplained, but no sentence 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 single-parameter read-only lookup with no output schema, the description does the necessary job of describing the returned fields (takeaway, status, grade, citation count, URL). Combined with full schema coverage and clear read-only annotations, an agent has enough to call it correctly, though the absence of sibling routing leaves a small gap.

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 slug parameter is already documented as 'Peptide slug or alias'. The description adds no information about slug format, alias resolution behavior, or failure modes, so the baseline 3 applies with the schema doing all the work.

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 enumerates what the summary contains (takeaway, status, evidence grade, citation count, URL) but never states the verb+resource the way the name does — it relies on 'get_peptide_summary' to convey that this returns a summary for a peptide slug. It hints at a distinction from heavier siblings like get_peptide or get_citations by being 'answer-first', but doesn't make that contrast explicit.

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 and no named alternative among the ten siblings; an agent must infer that this is the lightweight first-look tool versus get_peptide or get_citations. The parenthetical '(free)' is the only usage signal, implicitly suggesting it is cheaper than other lookups, but that is left unstated.

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 establish this as a safe, closed-world read. The description adds genuinely non-obvious behavioral context: a free daily allowance, then metered pricing via x402 USDC or an X-API-KEY credit. That cost/quota model is exactly the kind of thing an agent needs before calling, and it isn't derivable from the schema. It stops short of describing error or throttling behavior once the free tier is exhausted.

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's short with no wasted sentences, which is good, but it is telegraphic to the point of being cryptic — a sentence fragment followed by a parenthetical pricing note. The pricing caveat is arguably the most decision-relevant content yet is buried at the end.

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, read-only lookup with no output schema, the description supplies the returned data categories and the access/cost model, which is everything an agent needs to decide to call it. The remaining ambiguity is about sibling overlap, which is scored under purpose clarity rather than completeness.

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 there is a single required 'slug' parameter already documented as 'Peptide slug'. The description adds no meaning about slug format, resolution, or what happens on an unknown slug, 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?

The description names the resource and the data returned (regulatory status, UNII, CAS), but it is a bare noun fragment with no verb, so it's unclear whether it fetches, computes, or summarizes. It also fails to differentiate from siblings like get_identifiers and get_approvals, which plausibly overlap with UNII/CAS and regulatory data.

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 guidance on when to use this tool versus get_identifiers, get_approvals, or get_dossier. The only conditional content is about payment, not about tool 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?

Annotations already declare readOnlyHint=true and openWorldHint=false, and the description adds genuinely non-obvious behavioral context beyond them: a free daily allowance followed by metered pricing ($0.02 USDC via x402 or 1 credit with X-API-KEY). It stops short of describing coverage, depth, or response shape, but the cost/quota disclosure is exactly the kind of trait annotations cannot convey.

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 the purpose front-loaded and no filler. The billing parenthetical is boilerplate shared across the server's tools, which slightly dilutes but does not derail it.

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 read-only tool with full schema coverage and no output schema, the description is minimally sufficient, but it leaves the agent guessing how this differs from the other safety-adjacent siblings and what the notes actually contain. A single disambiguating clause would close the gap.

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 there is a single required parameter ('slug', documented as 'Peptide slug'), so the schema carries the semantics. The description adds nothing about the slug (e.g., format, lookup behavior on unknown slugs), so 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?

The description names a specific content domain (safety, toxicology, pharmacokinetics notes) that is distinguishable from siblings like get_adverse_events and get_regulatory, but it is a bare noun phrase with no verb stating that it retrieves notes for a given peptide. An agent can infer the resource but not the operation 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 get_adverse_events, get_regulatory, get_dossier, or get_peptide_summary, all of which could plausibly overlap with safety data. The only conditional content is billing, not selection 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?

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds meaningful behavioral context beyond annotations by disclosing live-registry scope, free daily allowance, and paid access via x402 or X-API-KEY. It does not specify how many recent trials are returned, but that is a minor 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?

The description is a single front-loaded sentence that names the returned data, followed by a compact parenthetical on billing. There is no redundant prose, though the billing parenthetical is dense.

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?

Because there is no output schema, the description compensates well by naming the returned aggregates and NCT links. Missing details such as the limit on 'most recent' trials are minor for this one-parameter read-only tool.

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

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

The sole parameter, slug, is fully documented in the schema as 'Peptide slug'. The description adds no further semantics such as format or examples, so the baseline of 3 applies given 100% schema description coverage.

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 resource, the ClinicalTrials.gov registry landscape, and enumerates the returned aggregates: total studies, phase/status counts, active-study count, and recent trials with NCT links. It is clear about what the tool returns, but it does not explicitly distinguish itself from sibling get_trials.

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 explicit when-to-use guidance and no named alternative such as get_trials. The only additional context is pricing, so an agent must infer that this is the overview tool rather than a detailed trial search.

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 declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds real value beyond them by disclosing the pricing model (free daily allowance, then $0.02 USDC via x402 or 1 credit with X-API-KEY), which affects whether an agent should call it. It still says nothing about payload shape, result size, or empty-result behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short fragments with no filler; the billing caveat is compact and parenthetical. The structure is slightly off-balance because roughly half the text is payment mechanics while the actual purpose phrase is truncated.

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 read-only, single-param lookup with no output schema, the agent still lacks a statement of what comes back (trial rows, regimen details) and how this differs from get_trial_landscape and get_dossier. The payment disclosure is a genuine completeness win, but the retrieval contract remains implicit.

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?

With one parameter at 100% schema description coverage, the schema already documents 'slug' as a peptide slug. The description adds no further meaning about slug format, resolution behavior, or what happens for an unknown slug. Baseline 3 applies when the schema does the documentation work.

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?

"Clinical/preclinical study rows and published regimen" names the resource but is a sentence fragment with no verb, so it never states that it retrieves trial data for a given peptide. It is distinguishable from get_adverse_events or get_regulatory by topic, but the boundary with get_trial_landscape and get_dossier is left unaddressed.

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 at all: no prerequisites, no conditions, and no mention of any sibling such as get_trial_landscape or get_dossier. The only contextual statement is about billing, which tells the agent nothing about 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?

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety and scope are covered externally. The description adds the '(free)' billing signal, which is genuinely useful context not present in annotations, but says nothing about pagination, ordering, or shape of the returned counts. With annotations carrying the safety profile, a 3 is appropriate.

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?

Four words with zero padding, and the resource is front-loaded ahead of the '(free)' qualifier. It is efficient, though so terse that it borders on under-specification rather than ideal concision.

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

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

For a no-argument, read-only listing tool with no output schema, the agent mainly needs to know what comes back; 'body systems with peptide counts' covers that at a minimal level. It leaves open the return shape (list of names plus integer counts) and ordering, which is the only real gap.

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 there is nothing for the description to disambiguate; baseline 4 applies. The schema is empty and fully described, leaving no gap the description needs to fill.

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 fragment 'Body systems with peptide counts' identifies the resource and what accompanies each entry, so an agent can guess it enumerates body-system categories. But it is a noun phrase with no verb and no differentiation from the sibling list_peptides, so the agent must infer that this is the taxonomy-level listing rather than a peptide listing.

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?

'(free)' hints that this call has no cost/credit implication, but there is no statement of when to choose this over list_peptides or search, no prerequisite, and no exclusion. Usage must be inferred entirely from the name.

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 declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds genuinely useful context in 'slim fields' (a reduced projection) and 'cursor pagination', plus a cost note '(free)'. It doesn't explain what slim fields include, the default page size, or whether this differs from get_peptide in what it returns.

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 compact clauses, front-loaded with the purpose, then the return shape and pagination, then the cost tier. There is no filler and nothing is buried.

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 read-only list tool with full schema coverage and annotations, this is nearly complete: purpose, pagination, and output breadth are all addressed. The one gap is that 'slim fields' is undefined and there is no output schema, so an agent cannot know exactly which fields come back or how this differs from get_peptide.

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%, with each of the five parameters documented, including q, limit range, cursor source, status example, and the body_system reference. The description only echoes filtering and cursor pagination at a high level and adds no format or behavioral detail beyond the schema, so 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 and resource: 'List and filter peptides', which is clearly a collection endpoint. It does not differentiate itself from siblings such as search or get_peptide, so an agent could not confidently choose it over search without opening 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?

No guidance on when to use this tool versus search, get_peptide, or compare_peptides is given. Usage is only implied by 'list and filter'; there are no exclusions or named alternatives.

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

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

Annotations already cover the safety profile (readOnlyHint=true, openWorldHint=false), and the description usefully adds that results are extractive quotes rather than generated text and that there is a free daily allowance followed by payment. However, it omits practical behavior an agent needs before calling: what happens when the free allowance is exhausted (hard failure vs. payment prompt), whether the paid path requires a token/header, and result-set/pagination 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?

One sentence plus a parenthetical covering cost and auth. The core value proposition is front-loaded and every clause carries information; 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?

With no output schema, the description correctly carries the burden of describing the return shape ('cited passages quoted from our monographs, extractive'), which is the key thing an agent must know. It is only slightly short on the payment mechanics and failure behavior mentioned above.

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 documented structurally and the description need not repeat them. It adds nothing about the semantics of 'q' (natural-language phrasing does matter here) or the effect of 'limit' on cost/latency, so it stays at the baseline for full schema coverage.

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 concrete transformation: a plain-English question returns cited passages quoted from monographs, and explicitly says it is extractive with no generation. That distinguishes it from generative answer tools, though it doesn't explicitly contrast with structured siblings such as get_citations or get_dossier.

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 'Plain-English question' framing implies the usage context, but there is no explicit when-to-use guidance, no exclusions, and no routing to siblings that also return citations (get_citations, get_trial_landscape). Usage must be inferred from the name and phrasing.

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