Peptide Library

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

Annotations already declare readOnlyHint, idempotentHint, non-destructive and closed-world, so the safety profile is covered. The description adds a genuine behavioral trait beyond that: it is 'arithmetic only' and explicitly makes no claim about chemical/physical compatibility, which stops an agent from over-reading the output as a mixing recommendation.

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, zero filler, front-loaded with the scenario ('several components dissolved together in one volume') before the scope caveat. Nothing is repeated from the title or schema.

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

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

For a deterministic calculation tool with an output schema present, the description need not explain return values, and it correctly covers the scenario and the limits of its claims. The remaining gap is the absence of any pointer to sibling tools (solution_concentration, convert_units) for related single-solute or unit-conversion needs.

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 50%, so the schema already documents volume_ml, amount and unit formats. The description adds the concept of a per-component share-by-amount output and the multi-component framing, but says nothing about the optional 'peptide' IU-factor parameter or how amounts in mg/mcg/IU relate, leaving half the surface unexplained.

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 specific computation: per-component concentration plus each component's share of the blend by amount, for multiple components in one volume. That distinguishes it in substance from the single-component sibling solution_concentration, though it never names that sibling. The verb is implicit (no 'compute'/'calculate'), but the resource and output are concrete.

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 'for several components dissolved together in one volume' implies the usage context (multi-component blends, unlike a single-solute concentration call), and 'it says nothing about whether components can or should be combined' sets a scope boundary. However, no alternative tool is named and no when-not-to-use condition is stated, 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?

Beyond the read-only/idempotent annotations, it discloses key behavioral traits: each row reports same/different/partial/not comparable/incomplete, missing data is never fabricated, and peptides are never ranked. This is exactly the kind of non-obvious behavior an agent needs.

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

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

Front-loads the purpose and scope, then adds the behavioral caveats. The middle sentence is dense with enumerations but each element 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?

An output schema exists, so return values needn't be explained, and the annotations cover the safety profile. The description still adds the comparison semantics and no-ranking guarantee, making it complete for this read-only comparison 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 description coverage is 100% and both parameters are fully documented there (ids/names/aliases, and the dimensions enum). The description's mention of structured fields loosely maps to dimensions but adds no syntax 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 (compare), resource (peptides), and scope (2-3 peptides side-by-side), and enumerates the exact profile fields compared. This clearly distinguishes it from single-entity siblings like get_peptide_details and search_peptides.

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 usage ('Suitable for tables', 'No dosing information' excludes out-of-scope intent) but never explicitly names when to choose this over get_peptide_details or search_peptides. Usage is inferable but no alternative is called out by 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 establish it as a safe, idempotent, non-destructive read, so the bar is lower. The description still adds real behavioral context beyond the annotations: mg/mcg conversion is exact, IU conversion is conditional on profile data, and the failure mode is a named issue (iu_factor_missing) instead of a silent assumed factor. It also disclaims any dosing interpretation.

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 dense sentences with no filler. The core operation, the conditional caveat, and the scope disclaimer are each stated once and in priority order.

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 an output schema present and full schema coverage, the description only needs to carry edge-case and scope context, which it does: the IU precondition, the resulting error name, and the non-dosing disclaimer. Minor omission is that it never states which direction pairs are valid (mg↔mcg vs IU↔mass), leaving that to the enums.

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%, including the note that `peptide` is only needed for IU, so the baseline is 3. The description reinforces the peptide parameter's role and its failure mode but adds no format or syntax detail beyond the schema (e.g., the "2,5" input notation comes from 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 verb and resource (convert an amount between mg and mcg) and immediately scopes the harder case (IU ↔ mass requires a documented IU-per-mg factor). It is clearly distinguishable from sibling tools like solution_concentration or blend_concentrations, which deal with mixtures rather than unit arithmetic.

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?

Gives an explicit conditional rule: IU conversion only works when a peptide profile documents an IU-per-mg factor, otherwise you get an iu_factor_missing issue rather than an assumed factor. It also pre-empts misuse by stating this is arithmetic, not a dosing recommendation. It does not explicitly route between this and sibling tools, which keeps it short of a 5.

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 a safe, idempotent, closed-world read, so the safety profile is covered. The description adds genuinely new behavioral context: it performs no estimation and no currency conversion, and only consumes supplied numbers. It does not state failures (e.g., what happens when amount_per_package is zero or the peptide factor is unknown).

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 tight sentences, front-loaded with what is computed, followed immediately by the constraints. No filler and nothing that could be cut without losing meaning.

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 an output schema present, return values need not be explained, and the description correctly covers what is calculated, which unit variants are supported, and the non-conversion/non-estimation constraint. It is complete for invocation; only edge-case failure behavior is left 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%, so parameters such as price, shipping, currency, amount_unit and the peptide IU factor are already documented in the schema. The description's mention of 'a peptide's documented factor' for per-IU pricing simply restates what the peptide parameter description says, adding no syntax or format detail beyond it. 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 precise verb+resource: purchase price arithmetic producing total cost and cost per mg/mcg/IU. The scope (total cost with optional shipping, unit costs, IU via a peptide's documented factor) is distinctive enough that an agent can separate it from convert_units or solution_concentration without opening a 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 description bounds usage clearly by negation: it uses only the numbers given, never estimates prices or shipping, and converts no currencies – which implicitly steers currency-conversion needs to convert_units. However, no sibling is named explicitly as the alternative, so it stops short of a 5.

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, idempotentHint, non-destructive and closed-world, so the safety profile is covered. The description adds that output includes descriptions, counts, profiles and links, but discloses nothing about auth, rate limits, or caching, so it contributes only modest behavioral context beyond the structured fields.

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

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

Two sentences, both earning their place: the first front-loads what is listed and its payload, the second gives the follow-on action. No filler, no restating of the title.

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 an output schema present, the description need not detail return shapes, yet it still summarizes the contents and cross-references the sibling that consumes category ids. Combined with full annotations and a zero-parameter schema, nothing needed to invoke it correctly is missing.

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

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

The tool takes zero parameters, which is the baseline-4 case: there is nothing for the description to disambiguate in the schema. The description correctly implies a parameterless, retrieve-everything call and even explains where the returned category id is consumed (search_peptides), which is useful parameter-adjacent context.

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 (List) and resource (Peptide Library research categories) and enumerates exactly what each entry contains: description, profile count, profiles, and links. It also differentiates from the discovery flow by pointing at search_peptides for filtered lookups, so an agent can place it against siblings like get_peptide_details.

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 states a concrete downstream use — "Use a category id with search_peptides to filter" — which tells the agent why and when to call this before searching. It stops short of explicitly naming when NOT to use it or which sibling to prefer for pure enumeration needs, so it is clear context rather than full 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 readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuinely useful behavioral context beyond that: missing fields are returned as 'not_documented' and dosing/administration content is never included, which tells the agent what it will never receive from this call.

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

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

Purpose is front-loaded in the first clause, followed by a dense but useful enumeration of returned sections and a closing exclusion sentence. It is long, but every element earns its place except the field list, which partly restates the available output schema.

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

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

With an output schema present, the return-value enumeration is somewhat redundant, yet the description still adds scope limits ('not_documented', no dosing) that the schema cannot express. Annotations cover the safety profile, so nothing essential is missing 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?

Only one parameter and schema coverage is 100% — the schema itself documents the id/name/synonym/alias forms. The description adds no further input semantics, 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 a specific verb and resource — a full educational research profile for a single peptide — and enumerates the content domains it returns. The scope phrase 'for one peptide' implicitly separates it from search_peptides/compare_peptides, but no sibling is named, so an agent must infer the boundary.

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 implied by the single-peptide scope, but there is no explicit when-to-use/when-not statement and no pointer to alternatives such as search_peptides or compare_peptides. An agent can guess the intent but is not routed.

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, idempotentHint, destructiveHint=false, and openWorldHint=false, so safety is covered. The description adds real behavioral context beyond that: results are aggregate and privacy-preserving, with no head counts, no individual activity, and small cohorts suppressed. It does not discuss refresh cadence or caching, but the privacy constraints are the material disclosure here.

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

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

Front-loaded with the scoping statement, then the return fields, then the privacy constraints and the safety caveat. The middle sentence is dense with clauses but each clause earns its place; nothing is repeated from the schema or annotations.

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 an output schema present and annotations carrying the safety profile, the description only needs to add meaning the structured data cannot. It does so by naming the fixed time window, the aggregation/privacy model, and the interpretive caveat, leaving nothing essential to calling the tool correctly.

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

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

The tool takes zero parameters, so the baseline is 4 and there is no parameter semantics to add. The description correctly avoids inventing inputs (e.g., no date-range or cohort args), implicitly confirming the fixed last-7-days window.

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 (this week's most-researched peptides in the Peptide Library community) and pins down the exact return shape: rank, share of researchers, growth percentages, and what moved. This distinguishes it cleanly from siblings like search_research (query-driven) and get_peptide_categories (taxonomy), which an agent can differentiate 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?

Usage is implied by the framing as an aggregate popularity snapshot, and the closing caveat 'Popularity is not evidence of safety or effectiveness' usefully signals when not to lean on this output. However, it never names an alternative tool or a concrete precondition (e.g., 'use search_research for specific compounds'), so the when/when-not 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?

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile needs no repeating. The description adds genuine behavioral context beyond that: what comes back (short educational profiles with evidence level and canonical URL) and a content limitation (no dosing data). It stops short of describing pagination behavior, but offset/limit are self-documenting in the schema.

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 tightly packed sentences: scope and searchable fields first, return contents second, sibling routing and exclusion last. No filler, no restatement of the tool name, and the most decision-relevant facts are front-loaded.

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

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

An output schema exists, so return values need no elaboration, and the description still summarizes them usefully. With all five parameters documented in the schema and the alternative tool named, an agent has everything required to select and invoke 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 100%, including examples for query and a pointer to get_peptide_categories for the category enum, so the schema does the heavy lifting. The description restates the same field semantics (name/synonym/alias, fuzzy, category, research area) without adding format or syntax detail beyond it, 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 (search) over a named resource (Peptide Library research database) with the searchable fields (name, synonym, alias, fuzzy matching) and the optional filters. It is immediately distinguishable from get_peptide_details, which it explicitly names as the deep-dive sibling.

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?

Routes the agent to the correct alternative with an explicit condition: "Use get_peptide_details for the full profile." It also sets a scope exclusion ("No dosing information"), so an agent knows when this tool will not satisfy a request.

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 readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuinely useful behavior beyond that: results are titles, short snippets and attributed links, never full text, and a defined set of subject matter is deliberately absent from the index. It stops short of noting no-result behavior or ranking/limits, 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?

Two sentences, no filler, and the positive scope is front-loaded before the exclusions. Every clause carries information an agent needs — either what is searchable or what is deliberately not.

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 an output schema present, the description is not obliged to explain return fields, and it correctly focuses on corpus scope and exclusions. It omits any mention of result limits or empty-result handling, but for a read-only search tool with full schema coverage and an output schema, the definition is adequate.

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%, including examples for 'query' and an enumeration for 'types', so the schema already carries parameter meaning. The description's enumeration of content types mirrors the schema's 'types' description rather than adding syntax, defaults or matching semantics 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?

States a precise verb ('Search') plus the exact resource ('Peptide Library's own educational content') and enumerates the four corpora it covers (site FAQ, per-peptide FAQs, articles, courses). The 'own' qualifier and the exclusion of third-party articles clearly separate it from siblings like search_peptides or get_peptide_details.

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?

Gives strong in/out-of-scope guidance: it names the excluded topics (dosing, reconstitution, injection guides, buying/vendors/prices) and notes third-party articles are not covered, which tells the agent when this tool will return nothing useful. It does not, however, name a sibling to use instead for those excluded topics, so routing is left partly 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?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, and openWorldHint=false, so the safety profile is handled. The description adds meaningful behavioral context: the tool is pure arithmetic on given numbers, produces no advisory output, and IU/mL depends on a peptide's documented factor. Minor gaps remain (e.g., handling of invalid inputs like zero volume) but the key behavior is 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?

Two tightly written sentences. The formula and output units are front-loaded, followed by a concise scope boundary. Every sentence earns its place with no 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?

Given that an output schema exists (so return values need not be described), annotations cover safety, and the input schema has full description coverage, the description provides exactly the missing context: the calculation performed, output units, and the fact that it is purely arithmetic without preparation advice. Nothing essential is missing 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?

Schema description coverage is 100%, so the baseline would be 3, but the description adds value by stating the exact formula (amount ÷ volume), the resulting units, and the role of the optional peptide parameter for IU/mL. This meaningfully supplements the schema's field-level documentation.

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 calculation (concentration = amount ÷ volume) and the output units it produces (mg/mL, mcg/mL, IU/mL). It clearly distinguishes this pure arithmetic tool from advisory or preparation tools, but it does not name or contrast with any sibling tool (e.g., convert_units or blend_concentrations).

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 implied by the calculation context, and the description adds a useful negative constraint ('it does not suggest amounts, volumes or how to prepare anything'). However, it never explicitly says when to choose this tool versus alternatives like convert_units or blend_concentrations, so the agent must infer routing.

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