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

No annotations are provided, and the description does not disclose behavioral traits beyond what the schema implies, such as computation limits, required input constraints (e.g., aligned sequences), or error conditions. The return type is vaguely described without additional context.

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 concise, starting with the purpose and then listing parameters and return type. However, the args section is somewhat verbose and could be condensed without losing clarity.

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 the tool's moderate complexity and the existence of an output schema, the description provides adequate but minimal context. It does not discuss input validation or use cases, leaving gaps in comprehensiveness.

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 0% schema description coverage, the description adds significant meaning: it explains the sequences parameter explicitly, details the method options and default, and clarifies that labels are optional. This compensates well for the lack of schema descriptions.

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

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

The description clearly states the tool's action ('Build a phylogenetic tree') and identifies the key resource ('from sequences'). It distinguishes itself from siblings like calculate_distance_matrix and draw_tree by specifying the output is a tree structure.

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 does not explicitly state when to use this tool versus alternatives, such as when sequences are already aligned versus needing alignment first. The implication is present but not overt, and no exclusions or prerequisites are mentioned.

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 must fully disclose behavior. It mentions using a substitution matrix and returning a dictionary, but lacks details on the expected format of alignment_str, error handling, or case sensitivity. This leaves ambiguity for the AI agent.

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 brief and front-loaded with the purpose. Every sentence adds value, and the Args/Returns structure is clear.

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 an output schema exists, the description need not detail return values. However, it omits critical details about the required format of alignment_str, which could cause errors. It is adequate but not fully complete.

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

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

Schema description coverage is 0%, so the description must compensate. It adds meaning for both parameters: alignment_str is 'Aligned sequences (with gaps) as a formatted string' and matrix_name has a default value. However, it does not specify exact formatting or valid matrix names.

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

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

The description clearly states the verb 'Calculate' and the resource 'alignment score using a substitution matrix'. This distinguishes it from sibling tools that perform alignment creation (pairwise_align, multiple_sequence_alignment) or other analyses.

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

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

The description implies when to use the tool: to score an existing alignment. It does not explicitly say when not to use or mention alternatives, but the purpose is clear enough to differentiate from siblings.

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 disclosure but only mentions parameters and returns. It does not explain possible models, error conditions, or performance characteristics, leaving the agent with minimal insight beyond 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?

The description is relatively concise with no redundant text, but the structure mixes a brief purpose with parameter list in a docstring format, which is functional yet not maximally efficient.

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 the tool's complexity, no output schema details, and sibling tools requiring differentiation, the description is incomplete; it omits expected input formats, model options, and example usage, leaving significant gaps for an agent.

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%, so the description adds meaning by naming param roles (aligned sequences, model with default, optional labels). However, it lacks details on model options, format constraints, or label usage, only partially compensating 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 clearly states the tool calculates a pairwise distance matrix for sequences, specifying the resource and action. It distinguishes from siblings like build_phylogenetic_tree by focusing on distances, but could be more explicit about the requirement for aligned sequences.

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 alternatives. While it implies usage for computing distances from aligned sequences, it does not mention exclusions or compare to siblings like pairwise_align or calculate_alignment_score.

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 are absent, so the description must carry the full burden of behavioral disclosure. It only mentions the return format (dictionary with percentage and counts) but omits details like input validation, case sensitivity, potential errors, or performance considerations for long sequences.

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 short and front-loaded with the purpose. However, the args/returns block is somewhat redundant given the input schema and output schema exist. Still, it is efficient and avoids unnecessary detail.

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 the tool's low complexity (1 parameter, output schema exists), the description covers the basic purpose and return format. However, it lacks usage guidelines and behavioral details, leaving the description adequate but not fully complete.

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

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

With 0% schema description coverage, the description's 'DNA or RNA sequence string' adds meaningful context beyond the schema's type 'string'. It clarifies acceptable input types, which is helpful for a single parameter 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 clearly states 'Calculate the GC content of a DNA or RNA sequence', specifying both the verb and the resource. It distinguishes itself from sibling tools like 'reverse_complement' or 'translate_sequence' by focusing specifically on GC content calculation.

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 is provided on when to use this tool versus alternatives, nor are any prerequisites or exclusions mentioned. The description simply states what it does without contextual usage advice.

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 provided, and the description lacks any behavioral traits: no mention of side effects, permissions, rate limits, or computational cost. The tool performs a calculation but gives no insight into execution behavior.

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

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

The description is very short and front-loaded with the core purpose. Every sentence is functional, though the docstring-style 'Args' and 'Returns' are somewhat redundant given the schema but acceptable.

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

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

For a one-parameter tool with an output schema (as noted in context signals), the description adequately summarizes the return type as a dictionary. However, lacking examples or typical keys, it is minimally complete.

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

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

Schema description coverage is 0%. The description weakly adds 'Path to PDB file' which is already implied by the parameter name pdb_file and type string. No additional context (e.g., file format, location restrictions) is provided.

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

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

The description clearly states the verb 'Calculate' and resource 'statistics for a PDB structure file', distinguishing it from fetching or other operations. However, it does not specify what statistics are computed, limiting clarity.

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 vs siblings (e.g., fetch_pdb_structure). It does not mention prerequisites or typical use cases, leaving the agent to infer.

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, but the description fully discloses behavior: it clears cache, returns a dictionary with success/cleared count/database cleared/cache location, and notes on caching optionality, TTL, and storage.

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 well-structured with sections (Args, Returns, Examples, Notes) and front-loaded purpose. While slightly lengthy, each section provides value; minor conciseness improvements possible.

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

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

The description covers all necessary aspects: purpose, parameters, return values (despite output schema presence), examples, and behavioral notes. No gaps remain for a simple one-parameter 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 single parameter 'database' is explained: 'Database name to clear (empty string clears all databases)'. Examples illustrate usage. This adds significant meaning beyond the schema's default type.

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

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

The description clearly states 'Clear cached Entrez results' and explains the caching system. It uniquely identifies the tool's purpose among siblings, none of which deal with cache clearing.

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 advises using the tool to clear stale cache data and provides notes on caching behavior, but lacks explicit when-to-use vs. alternatives. However, no alternatives exist for cache clearing.

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 provided, the description carries the full burden. It discloses rate limits, caching behavior (use_cache and TTL inheritance), and AND logic for parameter combination. It does not explicitly state read-only nature, but it is implied. This is more transparent than typical.

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 well-structured with Args, Returns, Examples, and Notes sections. Every sentence adds value, and the core purpose is front-loaded. No unnecessary content.

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 6 parameters, no annotations, and an output schema that is described in detail (return dictionary structure), the description covers all necessary aspects: input, output, examples, and behavioral notes. It is fully complete for an AI agent to use 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?

Input schema has 6 parameters with 0% description coverage, but the description adds full meaning for each: variant notation examples, gene symbol, condition, significance filter list, default max_results, and use_cache. This fully compensates for the lack of schema descriptions.

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

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

The description clearly states it searches ClinVar for genetic variants and clinical interpretations, and specifies it is a specialized wrapper combining entrez_search and entrez_summary. This differentiates it from sibling tools like entrez_search and entrez_summary.

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 Notes section explains that at least one search parameter is required and multiple parameters are combined with AND logic. Examples demonstrate typical use cases. However, it does not explicitly state when to use this tool versus alternatives like entrez_search or entrez_summary, nor does it describe when NOT to use it.

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 must cover behavior. It mentions the return type but does not disclose any side effects, error handling, or constraints, which is acceptable for a simple drawing tool but not thorough.

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

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

The description is extremely concise: a one-sentence purpose, then a bullet-point parameter list, and a returns line. No wasted words, and all relevant information is included.

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 the tool has only two parameters, no annotations, and an output schema exists, the description is fairly complete. It covers inputs and return type, but lacks examples or error handling details, which are minor omissions.

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

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

The schema has 0% description coverage, so the description must add meaning. It explains both parameters: tree_newick is the tree in Newick format, output_format has a default of 'ascii' for text representation, adding significant value 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?

The description clearly states the tool draws a phylogenetic tree from Newick format, with a specific verb and resource. It distinguishes from sibling tools like build_phylogenetic_tree, which likely builds trees from other 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?

Usage is implied: use when you have a Newick string. However, no explicit when-to-use or alternatives are mentioned, leaving some ambiguity.

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 provided, the description fully shoulders behavioral transparency. It discloses rate limits (3 req/sec, 10 with API key), caching with 7-day TTL, return format handling (XML parsed, text raw), and batching advice. This is substantial, though it could mention potential timeout behaviors explicitly.

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 well-structured with clear sections (Args, Returns, Examples, Notes) and is concise given the complexity. Every sentence adds value, and the information is front-loaded with the essential purpose and parameters. No redundancy or fluff.

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 the tool's complexity (5 parameters, output schema present), the description covers all necessary aspects: parameter semantics, return value structure, caching behavior, rate limits, and multiple examples. It is sufficiently complete for an agent to select and invoke 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 coverage is 0%, so the description must compensate heavily. It does so by explaining each parameter (database, ids, rettype, retmode, use_cache) with types, defaults, and examples. The Returns section also adds meaning to the output schema, making the tool's usage clear without needing the schema alone.

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

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

The description clearly states 'Fetch full records from NCBI Entrez by UID' and provides parameter details and examples. However, it does not explicitly differentiate this tool from sibling tools like entrez_search or entrez_summary, relying on the user to infer the distinction based on the 'by UID' focus.

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 includes notes on batching, rate limits, and caching, which provide implicit usage guidance. However, it lacks explicit when-to-use vs. alternatives (e.g., when to use this vs. entrez_search or fetch_genbank), and no when-not-to-use scenarios are mentioned.

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, but the description fully discloses the tool's behavior: it returns a list of databases when database is empty, or detailed info otherwise. It also describes the return structure and field examples.

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 structured with clear sections (Args, Returns, Examples) but is slightly verbose for a single-parameter tool. However, the examples and clarity justify the length.

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 the output schema exists, the description still covers both possible return structures in detail. It also includes multiple examples for different inputs, making it fully complete.

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

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

Schema has 0% description coverage, but the description thoroughly explains the parameter's effect (empty vs specific string) and provides three usage examples, adding significant value 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?

The description clearly states the tool retrieves information about NCBI Entrez databases, with distinct behavior for empty vs specific database parameter. This distinguishes it from sibling tools like entrez_search and entrez_fetch.

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 for obtaining database metadata before searches but does not explicitly contrast with alternatives. It provides clear examples that guide usage.

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 bears full responsibility. It discloses rate limits (3/10 req/sec), that different databases support different link types, and notes that relationships are discovered automatically. It could mention that the operation is read-only and behavior on invalid IDs, but overall sufficiently 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?

The description is well-structured with Args, Returns, Examples, and Notes sections. Each sentence adds value, no fluff. The purpose is front-loaded, and the structure helps an agent quickly parse key 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?

Given the tool's complexity (cross-database linking), the description covers input parameters, returns (dictionary fields documented), usage guidance (rate limits, link name discovery), and examples. With an output schema present, the return documentation is sufficient.

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% (no descriptions in input schema), but the description extensively documents each parameter in the Args section, including types and examples. The ids parameter's flexibility (single ID, comma-separated, list) is clarified. Examples show typical usage.

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

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

The description clearly states that the tool finds related records across NCBI databases, with specific verb ('find related records') and resource ('across NCBI databases'). Examples like 'BRCA1 gene to PubMed' and mention of different databases distinguish it from sibling tools like entrez_fetch or entrez_search.

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?

Provides context on when to use (discovering cross-database relationships) and notes to use entrez_info for available link names. However, it does not explicitly state when not to use it or mention alternatives like entrez_search for direct queries.

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 provided, the description fully carries the burden of behavioral disclosure. It details rate limits (3 req/sec, 10 with API key), caching behavior (1 hour TTL), and the exact return dictionary structure (ids, count, total_found, query, database, cached). This is comprehensive and 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?

The description is well-organized with clear sections (intro, Args, Returns, Examples, Notes). It is concise yet comprehensive, with no wasted sentences. The most critical information (purpose, parameters, return format) is 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?

Given the tool's complexity (5 parameters, rate limits, caching, output schema), the description is fully complete. It covers purpose, all parameters with defaults, return format, example calls, and behavioral notes. The output schema exists (as per context) and the description aligns with 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?

Despite 0% schema description coverage, the description adds rich meaning to each parameter: database is elaborated with examples, query references Entrez syntax with examples, max_results states default and max, sort lists common values, and use_cache explains TTL. This fully compensates for the lack of schema descriptions.

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

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

The description clearly states 'Search any NCBI Entrez database using query syntax', with examples across multiple databases (pubmed, gene, nucleotide, clinvar). This differentiates it from sibling tools like pubmed_search (which is PubMed-specific) and entrez_fetch/entrez_summary (which retrieve details after search).

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 provides rich usage guidelines including parameters, examples, and notes on rate limiting and caching. It references the module docstring for syntax examples. While it does not explicitly state when not to use this tool (e.g., after search, use entrez_fetch to get full records), the context is clear enough for an AI agent to select it appropriately.

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 fully discloses behavioral traits: lightweight nature, speed advantage, rate limits (3 req/sec or 10 with API key), 7-day cache TTL, and database-dependent fields. All relevant for safe and effective use.

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?

Well-structured with sections (Args, Returns, Examples, Notes) and front-loaded. Slightly verbose but every sentence adds value; minor room for tightening without losing content.

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?

Covers all aspects: purpose, parameters, usage guidelines, behavioral traits, return format, and examples. With output schema present, the description of return values aligns well. Complete for a query 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 has 0% parameter descriptions; the description fully compensates by explaining each parameter: database (with examples), ids (single, comma-separated, or list), use_cache (default true, TTL 7 days). Includes multiple usage examples.

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

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

The description clearly states 'Get document summaries (DocSums) from NCBI Entrez', specifying the verb and resource. It distinguishes from siblings by noting it is lighter and faster than entrez_fetch for metadata-only queries, and provides explicit alternatives.

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?

Provides explicit when-to-use (metadata-only, lightweight), when-not-to-use (full content needed), and alternatives (entrez_fetch). Also mentions rate limits and caching behavior, giving clear context for appropriate invocation.

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 mentions that an email is required by NCBI and returns a dictionary, but does not disclose rate limits, error handling, or side effects. Some behavioral context is present.

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 well-structured docstring with clear Args and Returns sections. It is concise at about 8 lines with no unnecessary words, and the main action is 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?

Given the tool has an output schema (mentioned in context), the description's note on return type is sufficient. It covers the main purpose and parameters, though could benefit from mentioning error scenarios or rate limits for 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?

With 0% schema description coverage, the description compensates fully: it explains accession as 'GenBank accession number', email as 'Email address for Entrez (required by NCBI)', and rettype options ('gb' for GenBank, 'fasta' for FASTA) with default. This adds substantial meaning beyond the raw 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?

The description clearly states 'Fetch a sequence from GenBank by accession number', using a specific verb and resource. It distinguishes from sibling tools like fetch_pdb_structure and fetch_uniprot by specifying GenBank.

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 for GenBank sequences but does not explicitly state when to use or not use this tool versus alternatives like fetch_sequence_by_id. No exclusion or prerequisite guidance is given.

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 must disclose behavioral traits. It only mentions the return type (dictionary with structure info and file location) but does not discuss side effects, authentication, or rate limits. It is insufficient for a fetch operation's transparency.

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 concise and front-loaded, with three sentences covering purpose, parameters, and return type. Every sentence serves a purpose without 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 the tool's simplicity (2 parameters, output schema present), the description covers the essential information. However, it could provide more detail about the returned structure information, but the output schema likely fills that 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 0%, but the description adds examples for pdb_id ('1ABC') and clarifies file_format values ('pdb' or 'cif') with default behavior. This adds value beyond the schema's type and default fields.

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

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

The description clearly states 'Fetch a protein structure from the PDB database' using a specific verb and resource. It differentiates from sibling tools like fetch_uniprot or fetch_genbank by specifying the PDB database.

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 does not explicitly state when to use this tool versus alternatives. While the name is self-explanatory, no guidance on when not to use or alternative tools is provided.

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 provided, so the description carries full burden. It does not disclose side effects, error behavior, or authentication needs. The return type is mentioned but no details on edge cases like missing IDs.

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?

Concise with clear docstring format separating Args and Returns. No unnecessary text, but could be slightly more compact.

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 the complexity (3 parameters, no enums, output schema exists), the description is incomplete. It lacks usage context, error handling, and differentiation from many sibling tools. The output schema may help but is not shown.

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

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

The schema has 0% description coverage, so the description must compensate. It briefly explains each parameter (db, seq_id, email) but lacks details like valid database names, ID formats, or the effect of the default email. Minimal value added beyond names.

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

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

The description clearly states it fetches a sequence from NCBI by ID, using a specific verb and resource. However, it does not distinguish from sibling tools like fetch_genbank or entrez_fetch, which also fetch sequences.

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 alternatives. It does not mention prerequisites, required NCBI account, or rate limits. The only usage hint is that email is required by NCBI, but no further context.

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 that the tool fetches a sequence and returns a dictionary, and it lists parameter options. However, it does not detail error handling or potential failures.

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 succinctly structured with Args/Returns sections, no redundant information, and every sentence is informative. It is front-loaded with 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?

Given the tool's simplicity and the presence of an output schema, the description is largely complete. It covers the purpose, parameters, and return format. Missing are details on input validation or error conditions, but these are acceptable gaps for a basic fetcher.

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%, but the description explains both parameters: uniprot_id as the accession/ID and format with allowed values ('fasta', 'txt', 'xml') and default. This meaningfully supplements the bare 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?

The description clearly states 'Fetch a protein sequence from UniProt', specifying a unique resource (UniProt) and action. This distinguishes it from siblings like fetch_genbank (GenBank) and fetch_pdb_structure (PDB).

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 for retrieving UniProt sequences but does not explicitly compare to alternatives like fetch_sequence_by_id or state when not to use it. Guidance is implicit rather than explicit.

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 disclose behavioral traits. It implies a read-only operation but does not mention file requirements, error handling, performance, or side effects. The return type is noted but not detailed.

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 concise with a clear one-line purpose, structured Args/Returns sections. Every sentence adds value without 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 the tool's complexity (3 params, output schema exists), the description covers purpose, parameters, and return type. It lacks error handling and usage context, but is sufficient for basic understanding.

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 'Args' section adds meaning beyond the schema, explaining each parameter (e.g., 'Path to PDB file', 'List of residue numbers'). Given 0% schema coverage, this provides necessary semantics, though constraints like valid ranges are 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 clearly states the verb 'Extract' and the resource 'information about specific residues', with an example 'active site'. It distinguishes from sibling tools like 'find_motif' implicitly by focusing on residue-level data, but lacks explicit differentiation.

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 alternatives like 'find_motif' or other structure analysis tools. There is no mention of prerequisites, exclusions, or context, leaving the agent to infer usage.

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 exist, so description carries full burden. It explains core behavior (find motif, return positions/count) but lacks details on edge cases, algorithm, or performance.

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?

Description is brief, structured as Args/Returns, and front-loaded. Every sentence is necessary 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 low complexity, description covers parameters and return value. Could mention edge cases like empty sequence or motif not found, but overall 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 coverage is 0%, but description explains all three parameters: sequence, motif, and overlapping (with default). This adds semantic meaning beyond the schema's types.

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

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

The description clearly states 'Find all occurrences of a motif in a sequence' with specific verb and resource. It distinguishes from sibling tools like reverse_complement or translate_sequence.

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 for finding motifs but does not explicitly state when to use or exclude alternatives like find_active_site or multiple_sequence_alignment.

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?

Discloses rate limiting, precedence rules (gene_id over gene_symbol), caching behavior, TTL inheritance, and structured output format. No annotations exist, so description carries full burden and does so thoroughly.

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?

Well-organized with clear sections (Args, Returns, Examples, Notes). Every sentence adds value; no redundancy. Front-loaded with 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?

Covers all aspects: input parameters, output schema, examples, and behavioral notes. No missing information for a tool with this complexity and no annotations.

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%, but the description's Args section explains each parameter with defaults, examples, and precedence. Fully compensates for lack of schema descriptions.

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?

Clearly states 'Fetch comprehensive gene information from NCBI Gene database' with specific verb and resource. Differentiates from sibling tools like entrez_fetch by being a specialized wrapper for gene records with structured output.

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?

Provides clear context: 'Provide either gene_symbol or gene_id', organism disambiguation, rate limits, and caching. Lacks explicit when-to-use vs alternatives, but examples and purpose imply usage for gene info.

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 provided. Description discloses basic behavior (getting a URL) but lacks details on error handling, authentication needs, or rate limits. For a simple read tool, minimal disclosure is acceptable but could be improved.

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?

Description is concise with clear sections (Args, Returns, Examples). Every sentence adds value; no waste.

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?

Tool is simple with one parameter and an output schema. Description covers functionality, parameter, return value, and example, making it fully complete for agent use.

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%, but description adds 'Digital Object Identifier' and provides an example, compensating for schema gaps. Adds meaningful context beyond parameter name.

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

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

The description clearly states 'Get the URL for a DOI', specifying the verb 'get' and resource 'URL for a DOI'. It distinguishes from sibling tool 'get_pmc_url' which handles PMC IDs instead.

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 explicit guidance on when to use this tool versus alternatives. The context implies it's for DOIs (vs. PMC IDs for get_pmc_url), but no clear when-not or alternative mentions.

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 bears full responsibility. It indicates the tool returns a URL string but does not mention error behavior (e.g., invalid ID, network issues) or confirm it is a read-only operation. Acceptable but incomplete.

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

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

The description is extremely concise: one sentence for purpose, three short sections (Args, Returns, Examples), and every sentence adds value. No wasted words.

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

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

Given the tool's simplicity (one parameter, straightforward URL construction), the description fully covers what an agent needs: input flexibility and output format. Output schema exists, so return details are covered.

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%, so the description provides all parameter information. It explains that pmc_id accepts both 'PMC' prefixed and bare numbers, with examples demonstrating both forms. This adds substantial meaning beyond the schema's 'string' type.

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 starts with 'Get the URL for a PubMed Central article', which clearly identifies the tool's action (get) and resource (URL for PMC article). It stands out from siblings like get_doi_url and pubmed_fetch.

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 does not explicitly state when to use this tool versus alternatives (e.g., get_doi_url, pubmed_fetch). Usage is implied through examples but lacks exclusions or prerequisites.

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?

Without annotations, the description must disclose behavior. It notes this is a placeholder requiring external tools, which is transparent. However, it lacks details on side effects, performance, or error handling.

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 well-structured with Args/Returns/Note sections. It is concise, though the first sentence is redundant with the name. Overall efficient.

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 the context signals (low schema coverage, no enums, output schema exists but not shown), the description provides basic info but is incomplete. The placeholder note indicates it's not fully functional, reducing completeness for an AI agent.

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%, so description adds meaning: 'sequences: List of sequences to align' and 'algorithm: Alignment algorithm to use (default: 'clustalw')'. This clarifies purpose but does not specify sequence format or algorithm options.

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

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

The description clearly states the tool performs multiple sequence alignment, a specific operation on sequences. It distinguishes from sibling tools like pairwise_align (pairwise) and build_phylogenetic_tree (post-alignment). However, it does not elaborate on the exact output or scope.

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 alternatives. The note about requiring external tools hints at limitations but does not explicitly compare to sibling tools or advise on selection criteria.

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 provided, the description carries the full burden. It lists parameters and return type, but does not disclose behavioral traits such as computational complexity, sequence length limits, or side effects (though none expected). A basic description, but not deeply 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?

The description uses a standard docstring format with parameter listings, which is clear but somewhat verbose—many default values are already in the schema. While not excessively long, it could be more concise by omitting redundant default indicators.

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 7 parameters (2 required) with no schema descriptions, and an output schema exists but is not detailed in the description. The description covers all parameters and states the return type as a dictionary with alignment results, which is adequate for a standard bioinformatics 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?

Given that the input schema has 0% description coverage, the description compensates by naming each parameter (seq1, seq2, mode, match_score, etc.) and explaining their defaults and meaning (e.g., 'Score for matching residues'). This adds value beyond the bare 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?

The description explicitly states 'Perform pairwise sequence alignment' and lists two sequences and mode (global/local), clearly distinguishing it from sibling tools like multiple_sequence_alignment or build_phylogenetic_tree. The verb 'perform' combined with 'pairwise sequence alignment' precisely identifies the function.

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 use when pairwise alignment of two sequences is needed, but it does not provide explicit guidance on when to choose global vs local mode, nor does it mention alternatives like multiple_sequence_alignment. It lacks 'when'/'when not' statements.

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 discloses the OAI service dependency, open access requirement, rate limiting, and return format (success/error dictionary). It could be more specific about rate limits or automatic error handling, but covers essential behavioral traits.

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 well-structured with introduction, Args, Returns, Examples, and Note sections. It is front-loaded with the core purpose. Every sentence adds value, with no filler.

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

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

Given 3 parameters, no annotations, and existing output schema, the description is complete. It covers all essential aspects: what it fetches, prerequisites, parameters with examples, return structure, and limitations. The output schema is effectively described in the Returns section.

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%, so the description must fully explain parameters. It does: pmc_id accepts with/without 'PMC' prefix, format specifies 'xml' or 'text', timeout in seconds. It also explains return fields. This compensates completely for the lack of schema descriptions.

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

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

The description clearly states the tool fetches full-text articles from PubMed Central (PMC) using the PMC OAI service, specifying it works only for open access articles with a PMC ID. This distinguishes it from siblings like pubmed_search (search) and entrez_fetch (generic fetch), giving a specific verb and resource.

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 provides clear context: only for open access articles with PMC IDs, with rate limiting. Examples show usage. However, it does not explicitly compare to alternatives like entrez_fetch for other databases or pubmed_search for metadata, missing explicit when-not-to-use 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?

With no annotations, the description carries full burden. It discloses key behaviors: writes to disk, fetches in batches of 20, respects NCBI rate limits, includes YAML frontmatter, and uses environment variables for vault path. However, it does not state whether existing files are overwritten or what happens on write failure, leaving 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 well-structured with clear sections (Args, Returns, Examples, Notes) and uses markdown formatting. It is somewhat long (approx. 30 lines) but each part adds value. No redundant sentences; every section earns its place. Minor improvement could be trimming common knowledge (e.g., 'file should include .md extension' is implied).

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 the tool's complexity (6 parameters, output dictionary, sibling differentiation), the description is complete. It covers purpose, all parameters with examples, return value schema, behavioral notes (rate limits, batching), and environmental context. The presence of a detailed Returns section means no need to explain return values further. No gaps identified.

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?

Since input schema has 0% description coverage, the description entirely defines parameter semantics. It provides detailed explanations for all 6 parameters, including examples (e.g., query syntax), defaults, constraints (max_results max 1000), and valid options (sort values). This fully compensates for the schema's lack of descriptions.

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

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

The description clearly states it creates a formatted literature review from PubMed and writes to an MD file. The verb 'Create', resource 'literature review from PubMed', and output 'write to MD file' are specific. This distinguishes it from sibling tools like pubmed_search which only return results, or entrez_fetch which returns raw 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?

The description provides context about LLM-controlled location and file writing, and adds a note about splitting large reviews. However, it does not explicitly state when to use this tool vs alternatives (e.g., pubmed_search for simple queries, entrez_fetch for raw data), nor does it include 'when not to use' guidance. Usage is implied but not explicitly bounded.

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 discloses rate limits, caching behavior (TTL), and the use of Entrez query syntax. It lacks mention of any destructive potential, but the tool appears read-only.

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 well-organized with clear sections (Args, Returns, Examples, Notes) and front-loaded summary. It is slightly verbose but each section adds value.

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

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

The description covers input parameters, output structure, query syntax, caching, rate limits, and examples. Coupled with an output schema, it is fully adequate for an agent to select and invoke the 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?

Despite 0% schema description coverage, the description provides comprehensive details for all 6 parameters, including defaults, types, and usage context, far exceeding the schema's empty fields.

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

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

The description clearly states the tool performs a PubMed search with enhanced metadata extraction, distinguishing it from sibling tools like search_pubmed or pubmed_fetch by emphasizing structured metadata enrichment.

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 provides usage details (args, examples, notes on rate limits and cache) but does not explicitly compare with sibling tools or state when to avoid this tool in favor of 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?

No annotations are provided, so the description must convey behavioral traits. It only states the operation but does not disclose whether the function is idempotent, what metadata is included in the return, or any potential side effects. This is insufficient for an agent to understand the tool's behavior fully.

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 concise—two sentences plus an args/returns section. However, it packs little information, making it under-specified rather than efficiently informative. It does not waste words but also fails to provide necessary detail.

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 the tool is simple with one parameter and an output schema, the description is incomplete. It does not explain the return value beyond 'dictionary containing reverse complement and metadata,' leaving the agent unaware of the dictionary structure or possible keys.

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 0% schema description coverage, the description must compensate. However, it merely restates the parameter name and type ('sequence: DNA sequence string'), adding no additional semantics such as expected format (e.g., uppercase, no spaces) or constraints.

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

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

The description clearly states the purpose: 'Get the reverse complement of a DNA sequence.' This is a specific verb+resource combination that distinctly identifies the tool's function. It is easily distinguishable from sibling tools like transcribe_dna, translate_sequence, or calculate_gc_content.

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 provides no guidance on when to use this tool versus alternatives. With multiple related tools (e.g., transcribe_dna, translate_sequence) available, an agent receives no help in selecting the correct tool for the task.

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 must fully convey behavior. It mentions the email requirement and the return format (dict with PMIDs and article info), but lacks details on rate limits, error handling, or pagination. It partially covers behavioral aspects.

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 well-structured with Args and Returns sections, making it easy to parse. However, it repeats default values already in the schema, slightly reducing 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?

While an output schema exists (not shown), the description only vaguely describes the return value as a dictionary. Given the complexity of searching PubMed, more details on result structure, pagination, or limits are needed for 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?

With 0% schema description coverage, the description adds minimal meaning beyond the schema. It explains that email is required by NCBI, but otherwise repeats default values already present in the schema. This is insufficient to compensate for the lack of schema 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 clearly states the tool searches PubMed for scientific articles. It uses a specific verb and resource. However, among siblings there is also 'pubmed_search', which could cause confusion, and the description does not differentiate from that 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?

No guidance is provided on when to use this tool versus alternatives like 'pubmed_search' or 'pubmed_fetch'. The description only explains what the tool does, not the context of its use.

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 discloses that the tool returns a dictionary with sequence and metadata, but does not mention error handling or input validation, which is adequate for a simple transformation.

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 very concise, using bullet-style Args and Returns for clarity. Every sentence adds value, and the purpose is 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?

Given the tool's simplicity and the presence of an output schema (as indicated by context signals), the description is complete. It covers the main functionality and return type without missing critical details.

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 description adds meaning beyond the schema by explaining 'sequence' accepts DNA or RNA and 'reverse' transcribes in the opposite direction. This compensates for the 0% 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 clearly states the tool transcribes DNA to RNA or reverse transcribes RNA to DNA, providing a specific verb and resource. It distinguishes from siblings like reverse_complement and translate_sequence by focusing on transcription.

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 for transcription tasks but offers no explicit guidance on when to use this tool versus alternatives like reverse_complement or when not to use it.

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 discloses key behaviors: translation uses a genetic code table, can stop at first stop codon, and returns a dict with protein sequence and metadata. However, it does not mention error handling or input validation.

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 well-structured with a clear purpose sentence followed by Args and Returns sections. It is slightly verbose but front-loaded and free of 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 the tool's moderate complexity (3 parameters) and the presence of an output schema, the description provides sufficient context: it covers all parameters, return value, and key behavior. No critical gaps remain.

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%, but the description fully explains each parameter: sequence as DNA/RNA string, table as genetic code table with default 1, to_stop as stop at first stop codon. This adds significant meaning beyond the schema's type and defaults.

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

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

The description clearly states 'Translate a DNA or RNA sequence to protein,' providing a specific verb and resource. It distinguishes itself from sibling tools like transcribe_dna and reverse_complement.

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 is given on when to use this tool versus alternatives, nor are any prerequisites or exclusions mentioned. The description simply states what it does.

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 full disclosure weight. It discloses the use of Entrez ELink, rate limits, and the fact that not all variants have literature. It does not mention any destructive actions, which is appropriate for a read-only lookup tool.

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

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

The description is well-structured with clear sections (Description, Args, Returns, Examples, Notes). It uses bullet points for clarity and is concise without unnecessary text. Every section adds value.

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

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

The description covers purpose, all parameters, return structure, examples, limitations, and rate limits. Given the presence of an output schema, it appropriately summarizes return fields without over-explaining. The context is complete for the tool's complexity.

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

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

Schema coverage is 0%, but the description provides detailed parameter explanations in the Args section, including types, defaults, and examples. It explains variant_id as ClinVar ID or dbSNP rs number, source_db options, and max_results behavior. This fully compensates for the lack of schema description.

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

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

The description clearly states the tool finds literature linked to a specific variant, specifying the verb (find), resource (PubMed articles), and input (variant). It distinguishes from siblings like pubmed_search or entrez_link which have broader or different purposes.

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 provides parameter details, examples, and notes on limitations and rate limits, but does not explicitly guide when to use this tool versus alternatives like entrez_link or pubmed_search. The context of many sibling tools suggests that explicit guidelines would be beneficial.

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