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

Annotations already indicate readOnly, idempotent, and openWorld. The description adds behavioral details beyond annotations, such as the return being a summary (not per-residue) and the inclusion of a druggability pre-screen. It does not mention rate limits or authorization needs, but goes beyond the structured annotations.

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, using bullet points for clarity and front-loading the main purpose. Every sentence adds value, and there is no extraneous or redundant 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 presence of an output schema (documenting return values), the description provides sufficient context for a one-parameter tool. It clarifies that the output is a summary, not per-residue, and includes the druggability component. Minor omission: no mention of limitations like species specificity or data source, but overall complete for the task.

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

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

The input schema already provides a thorough description and pattern for the uniprot_id parameter, serving as a 100% description coverage despite the context indicating 0%. The tool description does not add any additional meaning beyond what the schema already offers, so it meets the baseline for schema-rich tools.

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 analyzes AlphaFold structural confidence using pLDDT and PAE, listing specific output components (pLDDT summary, PAE, druggability pre-screen). It distinguishes this tool from siblings like score_binding_pocket_geometry or detect_intrinsically_disordered.

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 provide any guidance on when to use this tool versus alternatives, nor does it specify contexts where it should be avoided. There is no mention of prerequisites or when alternatives like compare_disease_target_overlap might be more appropriate.

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, and openWorldHint. The description adds valuable context: it integrates four signals, does not add scientific judgement, and is not a validated predictive model. This goes beyond annotations and clarifies 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 bullet points and clear sections. It is concise but includes necessary details about the four signals and their sources. No redundant sentences.

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 (integrating multiple signals) and the presence of an output schema, the description adequately covers what the tool does and its limitations. It could mention expected output format or compare to sibling tools, but it is mostly 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 provide parameter explanations. The description mentions signals that involve drugs and clinical compounds, which relates to include_clinical_stage, but it does not explicitly describe the parameters or their usage. Neither uniprot_id nor include_clinical_stage are explained.

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 provides a comprehensive druggability assessment for a protein target, outputting a HOT/WARM/COLD/NOT_DRUGGABLE classification. It details the four integrated signals, distinguishing it from sibling tools that focus on individual aspects like structural confidence or disease targets.

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 explains what the tool does but does not explicitly state when to use it versus alternatives. It mentions that it assembles existing public-database evidence and does not add scientific judgement, implying it is for simple evidence gathering rather than deep analysis, but this is not 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?

Annotations already provide readOnlyHint, idempotentHint, openWorldHint. Description adds that it uses ACMG/AMP 2015 criteria and computational evidence, and clarifies it is a draft not a final review. No contradictions with annotations.

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 only three lines, each sentence adds value. First sentence states purpose, second details method, third provides usage caveat. 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 complexity of variant classification, description covers purpose, method, and usage context. Output schema exists to document return values. Could mention input validation, but schema handles it. Overall 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?

Input schema already has detailed descriptions for both parameters (hgvs with example, inheritance_pattern with enum meanings). The description does not add new parameter-specific information, so baseline of 3 is appropriate.

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

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

Starts with 'Generate a draft ACMG/AMP variant classification framework' - specific verb and resource. Clearly describes populating ACMG/AMP 2015 criteria from computational evidence, differentiating it from sibling tools like 'triage_variant_3d' and 'generate_variant_clinical_report'.

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?

States 'Designed to pre-populate variant interpretation forms for clinical laboratory review — NOT a substitute for expert review.' Provides context for when to use (drafting) and a caution against misuse, but does not explicitly list alternatives or when not to 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?

Annotations indicate readOnlyHint, openWorldHint, and idempotentHint, and the description's verb 'identifies' and 'returns' confirms non-destructive behavior. The description adds transparency by detailing the return structure (shared, unique, Jaccard score), which goes beyond annotations.

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 first sentence, bullet points for outputs, and a code example. Every sentence adds value, no filler, and the format is easy to parse.

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 (two disease inputs, multiple outputs including Jaccard score) and the presence of an output schema, the description covers all necessary aspects: purpose, use cases, outputs, and an example. It is thorough without being redundant.

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

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

The input schema already includes detailed descriptions for all three parameters (mondo_id_a, mondo_id_b, target_limit). The description provides an example with inline comments but adds little new semantic information beyond the schema. Baseline 3 is appropriate given high schema coverage.

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

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

The description clearly states it compares protein target landscapes of two diseases, lists specific outputs (shared targets, unique to each, Jaccard similarity), and provides an example with comments. It distinguishes itself from siblings like get_common_disease_targets by its specific analysis and return types.

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

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

The description explicitly mentions use cases such as drug repurposing, identifying shared mechanisms, and understanding comorbidity, guiding the agent on appropriate use. However, it does not explicitly state when not to use or compare with sibling tools, slightly reducing clarity.

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 indicate readOnlyHint and idempotentHint, reducing the burden. The description adds value by explaining the output is a pairwise distance matrix, the metric details, and emphasizing it is not Wasserstein. No contradiction with annotations, and it provides useful behavioral context beyond them.

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 (approx. 150 words), well-structured with purpose, metric details, applications, and caveats. Each sentence adds value, front-loaded with the core action. No redundancy or 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 the tool complexity, presence of output schema, and annotations, the description covers purpose, metric, applications, and limitations thoroughly. It provides sufficient context for an agent to decide when to use it and interpret results, without needing to replicate schema or output 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?

Schema description coverage is 0% per context, so the description should compensate. It mentions 'provided proteins' but adds no details about uniprot_ids beyond what the schema already states (list of UniProt accessions, 2-10). Without additional format or validation info, it fails to compensate for low schema coverage.

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

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

The description explicitly states the tool compares multiple proteins using TDA-fingerprint distance, specifies the distance metric (L2 on 64-dim vectors), and contrasts with Wasserstein distance. It distinguishes from sibling tools like compute_topology_fingerprint by focusing on pairwise comparison. This is specific, with a clear 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 lists applications (drug-repurposing triage, off-target screening, cross-species comparison) and states it is not a functional or sequence-similarity measure, guiding appropriate use. However, it does not explicitly advise against using it for other purposes or compare with specific sibling tools, missing some discrimination.

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 behavior beyond annotations: fetches AlphaFold model, runs persistent homology (with fallback if gudhi missing), returns no-structure result when model absent, explains Betti numbers and invariance. Annotations already indicate readOnly and idempotent, but description adds rich behavioral 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?

Description is well-structured with clear paragraphs and front-loaded purpose. While slightly long, every sentence provides valuable information about behavior, use cases, and limitations. Could be trimmed slightly but is justified.

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 existence of an output schema, description still explains return values (fingerprint vector, Betti numbers, residue count, method flag) and edge cases (no-structure result, fallback). Covers all necessary context for a complex 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 one parameter 'uniprot_id' with description and regex pattern. Description adds context by explaining it is used to fetch AlphaFold model, which provides meaning beyond the schema description. However, schema coverage is already good, so marginal added value.

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?

Description clearly states 'Compute a rotation-invariant topological fingerprint of a protein's fold.' It uses specific verb (compute), resource (topological fingerprint), and domain (protein). Mentions sibling tools that consume these fingerprints, distinguishing it from other structure-related tools.

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

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

Explicitly says 'Use it as the per-protein input to structure-similarity comparisons: compare_proteins_topologically and find_evolutionary_structural_shifts consume these fingerprints.' Also states what it is not a substitute for (sequence alignment, RMSD, functional-homology), providing clear when-to-use and 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?

The description adds significant behavioral context beyond annotations: the pLDDT proxy method, threshold <50, functional categories, and clinical relevance. Annotations already indicate read-only and idempotent, no contradiction. Could mention data source limitations.

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 and bullet points. Some clinical relevance details may be secondary but do not significantly impair conciseness. Could be slightly shorter but efficiently organized.

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 and existence of an output schema, the description covers purpose, method, output categories, and relevance. It does not explain the output schema format but that is handled by the schema itself. Sufficient for 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?

Schema description coverage is 0%, but the input schema itself describes the uniprot_id parameter with an example. The tool description does not add parameter details beyond what the schema provides, so baseline score of 3 is appropriate.

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

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

The description clearly states the tool maps intrinsically disordered regions using pLDDT as proxy. It specifies the verb 'map' and resource 'IDRs', and distinguishes itself from siblings by focusing on disorder prediction, but does not explicitly contrast with other structural analysis tools.

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

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

The description implies usage for IDR detection with clinical relevance examples, but lacks explicit guidance on when to use this tool versus alternatives like analyze_structural_confidence. No 'when not to use' information 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?

Annotations already declare readOnlyHint and idempotentHint. The description adds return format and example usage, but does not disclose pagination, error handling, or auth requirements. Adds modest value beyond annotations.

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 front-loaded purpose statement, bullet lists, and a helpful code example. Slightly longer due to example but earns its place.

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

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

Given the tool's complexity (nested params, output schema exists), the description covers purpose, use cases, and provides an example. It does not need to detail return values since output schema exists. Adequately 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 already provides descriptions for both parameters (tables and limit_per_table). The description includes an example using the 'tables' parameter but adds no new semantic meaning. Baseline 3 applies as schema coverage is sufficient.

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 exports knowledge-graph data for downstream analysis, with specific verb 'Export' and resource 'knowledge-graph data'. It distinguishes from sibling lookup/query tools by focusing on bulk export.

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 explicit use cases (loading into pandas, R, etc.) implying when to use. However, it does not explicitly state when not to use or name alternative tools, though sibling tools are distinct.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds context about the graph traversal and store population, explaining that the tool is non-destructive and does not modify data. It also clarifies the resolution behavior for each seed type.

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 front-loaded with the main action and then elaborates on seed types and store population. It is well-structured and concise, though the second paragraph 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 tool's complexity (2 parameters, output schema exists) and the presence of sibling tools, the description provides sufficient context for an agent to understand the tool's purpose, usage, and behavior. It covers seed resolution and data source, making it complete.

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

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

The input schema already describes both parameters (seed and max_hops). The description adds context about seed resolution but does not add significant meaning beyond what the schema provides. Since schema coverage is effectively 100%, baseline score of 3 is appropriate.

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

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

The description clearly states the verb 'Traverse the local knowledge graph from a seed entity' and specifies the resource (local knowledge graph). It explains the behavior for different seed types (gene symbol, UniProt ID, MONDO disease ID), distinguishing it from sibling tools that may query specific entities.

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 explicit usage context: for a given seed type, it resolves to specific entities (proteins, variants, drugs). It also mentions the store is populated by curated data and explicit writes, but does not explicitly exclude sibling tools or provide 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?

The description goes well beyond the annotations (readOnlyHint, openWorldHint, idempotentHint) by detailing the ranking algorithm, scoring formula, candidate list size, empty result behavior, and caveats about the composite score. This provides comprehensive behavioral insight.

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 and front-loaded with the core purpose. While it is relatively long, every sentence adds value given the tool's complexity. There is no redundancy, so it earns a 4.

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 and the presence of an output schema, the description is remarkably complete. It covers purpose, usage, algorithm, return structure, edge cases, and caveats, ensuring the agent has all necessary context.

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

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

The input schema already provides clear descriptions for all three parameters with defaults and constraints. The tool description does not add new parameter-level semantics; it only references parameters in the algorithm. With schema coverage at 0% (description adds no param info), the baseline of 3 is appropriate.

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

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

The description clearly states it ranks clinical-stage drugs as repurposing candidates for a disease, and explicitly distinguishes from the sibling tool map_disease_drug_landscape. This meets the criteria for a specific verb+resource+scope with sibling 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?

The description provides explicit guidance: use this tool for repurposing candidates; for the full drug picture use map_disease_drug_landscape instead. This qualifies as explicit when/when-not/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?

The description goes beyond annotations by detailing the fallback mechanism (tda_fingerprint vs sequence_identity), the partial coverage of AlphaFold DB, and the output field divergence_method. This provides rich behavioral insight that annotations alone (readOnlyHint, openWorldHint) do not cover.

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 an initial clear sentence, then details about methodology and caveats. It is informative but slightly verbose; some sentences could be tightened 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 complexity (cross-species, two methods, fallback) and the presence of an output schema and annotations, the description covers all essential aspects: purpose, methodology, coverage caveats, and result interpretation. No critical information 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 input schema already provides descriptions for both parameters (gene_symbol, target_species). The tool description adds context about the overall process but does not elaborate on the parameters themselves, offering minimal added meaning 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 starts with a clear verb and resource: 'Quantify cross-species structural and sequence divergence for a gene.' It goes into specific methods (TDA fingerprint distance, sequence identity), distinguishing it from sibling tools that focus on single-protein analysis or other aspects.

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. It explains the process and coverage limitations but lacks direct guidance on when a user should choose this over similar tools like compute_topology_fingerprint or compare_proteins_topologically.

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 indicate read-only and idempotent behavior, and the description adds valuable detail on report content, data sources, and the non-clinical nature. No contradictions.

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 a clear opening sentence, important disclaimer, and organized numbered list. Slightly lengthy but every sentence 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?

Given the tool's complexity and the presence of an output schema, the description explains the report contents, data sources, and warnings adequately. Could mention output format briefly but acceptable.

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 does not elaborate on parameters beyond the input schema, which already provides clear descriptions for all three parameters. Baseline of 3 is appropriate as schema coverage is thorough.

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 the tool generates a multi-source variant interpretation report, lists the seven databases cross-referenced, and distinguishes from siblings like classify_variant_acmg which focuses on ACMG classification alone.

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

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

Provides context that the report is a research aid and not for clinical use without review, and lists the databases included. Does not explicitly state when to use alternatives, but the detailed database list implies when this comprehensive tool is appropriate.

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 provide readOnlyHint and idempotentHint. The description adds that it queries Open Targets live, returns a JSON string with error handling (per-disease errors inline, not raised), and returns a JSON error object for invalid categories or filters. No contradiction with annotations.

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 well-structured. The first sentence states the core purpose, followed by usage guidelines, limitations, and behavior. 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 existing annotations and output schema (not shown but present), the description is complete. It explains the return format (JSON string with category, disease count, profile object), error behavior, and alternative tools, leaving no critical gaps for agent usage.

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

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

The input schema already provides descriptions for all three parameters, so the description doesn't need to add much. It mentions the 'category' parameter with an example and the 'disease_name' filter, but does not add new syntax or format details beyond the schema. Baseline 3 is appropriate given high schema coverage.

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

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

The description clearly states the tool profiles top drug targets for a curated set of common diseases in one call, using verb 'profile' and specifying the resource. It distinguishes itself from the sibling tool 'get_disease_targets' by noting it is the category-level multi-disease counterpart.

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?

Explicitly says 'Use this for a fast landscape scan across a whole disease area' and contrasts with 'get_disease_targets' for single diseases. It also states what not to do: 'does not accept a raw MONDO ID', providing clear when-to-use and 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?

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint. The description adds concrete behavioral details by listing evidence score types (e.g., genetic_association, somatic_mutation) and their data sources, going beyond the high-level hints. No contradiction with annotations.

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: first sentence states purpose, followed by a bullet list of evidence scores, and ends with an example. It is concise and front-loaded, with no wasted words. Slight reduction for not being even more succinct, but still very good.

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 available, the description does not need to detail return values. It covers evidence score breakdown and provides an example, but lacks guidance on parameter tuning (e.g., when to adjust min_score) or error handling. Adequate but not fully comprehensive.

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

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

The input schema already provides detailed descriptions for each parameter, including examples (e.g., disease_id 'MONDO:0007254'). The description adds minimal parameter information beyond the schema, only showing an example call. Baseline 3 is appropriate given high schema coverage.

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

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

The description clearly states the tool's action ('Return top protein targets for a disease') with a specific source ('Open Targets evidence scores'). The title 'Disease Target Evidence' reinforces the resource. It distinguishes from sibling tools like lookup_disease or search_diseases, which focus on diseases rather than targets.

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 through an example and evidence score breakdown but does not explicitly state when to use this tool over alternatives like get_target_diseases or get_common_disease_targets. No direct comparisons or exclusions are 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?

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, so the safety profile is clear. The description adds behavioral detail about the output (HPO phenotypes, constraint scores, interpretation) beyond what annotations provide.

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 structured with a clear first sentence, a usage sentence, a bullet list of returns, and an example. It is relatively concise but the bullet list partially repeats the first sentence.

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 presence of annotations and output schema, the description covers purpose, usage context, and output structure. It omits explicit mention of the boolean parameter's default behavior (though schema covers it) and does not clarify the return format.

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 already describes gene_symbol with examples and include_constraint with default and description, so parameter semantics are well-covered. The description adds only a code example, which is helpful but does not significantly increase understanding 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?

Description states 'Return all HPO phenotypes associated with a gene, plus gnomAD constraint.' This is a specific verb+resource combination that clearly distinguishes it from sibling tools like lookup_phenotype or search_diseases.

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?

Description includes 'Useful for understanding the clinical consequences of variants in a gene before requesting structural context,' which provides clear context and workflow positioning. However, it does not explicitly mention when not to use the tool or list alternatives.

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

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

Annotations already declare readOnlyHint and idempotentHint. Description adds useful specifics about what statistics are returned (entity counts, size, activity) without contradicting annotations.

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

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

Two sentences, front-loaded with purpose, no wasted words. Every sentence earns its place.

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

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

No parameters, annotations cover safety and idempotency, output schema covers return values. Description adds the remaining context about what stats are included, 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?

No parameters exist, baseline 4. Description adds meaning by specifying what the returned statistics cover, which is valuable for understanding the tool's output.

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?

Description clearly states it returns statistics about the local knowledge graph, listing entity counts, database size, and last activity. This distinguishes it from sibling tools which perform more specific analyses or queries.

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?

Description indicates usefulness for understanding current contents and coverage, providing clear context for when to use. Does not explicitly state when not to use, but no sibling serves the same stats purpose.

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 include readOnlyHint=true, idempotentHint=true, and openWorldHint=true, indicating safe, read-only behavior. The description lists the return fields (MONDO, HPO, targets, UniProt) but does not add significant behavioral context beyond what annotations and the output schema provide. No annotation contradiction.

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: a clear lead sentence, a brief context paragraph, a bulleted list of return fields, and an example. It is concise but the context paragraph could be slightly tighter without losing value. Overall, it is easy to parse and front-loaded with the main action.

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 presence of an output schema that likely details return formats, the description covers the tool's purpose, inputs, and outputs comprehensively. It explains the rationale for the tool (under-studied rare diseases) and provides a concrete example. The sample call and return structure make it complete for an AI agent to understand 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?

The input schema includes a description for 'orphanet_id' ('Orphanet disease ID, e.g. '79318' (Gaucher disease).'), matching the example in the tool description. The description does not add new semantic meaning beyond the schema, so it meets the baseline (3) for a well-documented parameter.

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

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

The description clearly states the verb 'map' and the resource 'Orphanet rare disease to its MONDO record, HPO phenotypes, and protein targets'. It distinguishes from sibling tools like 'get_disease_targets' and 'lookup_disease' by focusing on rare diseases and providing a comprehensive structural atlas. The title 'Orphan Disease Structural Atlas' further reinforces the specific purpose.

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 on when to use the tool: for rare diseases that are under-studied, and it returns a consolidated report. It includes an example ('get_orphan_disease_atlas(orphanet_id='79318')'). However, it does not explicitly state when not to use it or mention alternative tools, which would improve guidance.

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

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

Annotations (readOnlyHint, idempotentHint) are present. The description adds detailed behavioral context: returns stable download URLs, can embed coordinates if include_coordinates is true, and explains the no-model scenario. No contradictions.

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 first sentence and bullet-style details. It is informative but slightly verbose; every sentence adds value, though it could be more concise. Still, very good for a complex tool.

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 and presence of an output schema, the description covers all necessary aspects: metadata fields, optional coordinates, sibling tool differentiation, and an edge case for missing models. No 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?

While the schema already has descriptions for both parameters, the tool description adds extra context: the effect of include_coordinates (large response size) and the note about 'structure_available: false'. This enriches understanding 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 it retrieves an AlphaFold model: metadata, download URLs, and optional coordinates. It uses specific verb 'Retrieve' and resource 'AlphaFold Protein Structure', and distinguishes from sibling interpretation tools.

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

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

Explicitly calls it the 'single entry point' for getting the structure, and lists sibling tools for interpretation. It also notes the 'structure_available: false' case when no model, providing clear guidance on when and why the tool should be used.

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, and openWorld hints. The description adds that it uses Open Targets and returns a 'full disease landscape,' but does not clarify that the 'limit' parameter may restrict results (creating a minor inconsistency). Overall, behavioral traits are well communicated.

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

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

The description is three sentences: purpose, use case, and example. It is concise, front-loaded, and every sentence 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?

With output schema and annotations, the description covers key aspects: input, output, and example. However, it does not address the 'limit' parameter's effect on completeness (calling it 'full' but limit applies), which is a minor gap.

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

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

The input schema provides detailed descriptions for each parameter (e.g., limit, ensembl_id, uniprot_id), so the description does not need to repeat. The description adds a usage example but no extra semantic value. Baseline of 3 is appropriate due to high schema coverage.

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

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

The description clearly states the tool's function: 'Return all diseases associated with a protein target via Open Targets.' It specifies the input (UniProt accession) and output ('full disease landscape'), and distinguishes it from siblings like get_disease_targets by focusing on target-to-disease mapping.

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 indicates usage context: 'essential for target-validation and indication-expansion.' While it does not explicitly compare to alternatives or state when not to use, the purpose is clear enough to guide appropriate selection.

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

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

Annotations already declare readOnlyHint and idempotentHint. Description adds detail on return content (canonical MONDO entry with specific fields, cross-references, hierarchy), which is consistent. No contradictions or omissions about side effects or auth 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?

The description is concise: a single introductory sentence, a bullet list of return fields, and an example. No redundant information; each sentence serves a 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 presence of an output schema, the description adequately covers the return structure. It mentions hierarchy (which is not fully detailed in schema) and cross-references. Minor omission: no mention of error cases or behavior for invalid MONDO IDs.

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

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

The input schema already provides descriptions for both parameters (mondo_id and include_hierarchy). The description adds no additional meaning beyond the example usage. With schema coverage high, baseline 3 is appropriate.

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

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

Description explicitly states the tool retrieves a disease record from the MONDO ontology, specifies return fields (name, definitions, cross-references, hierarchy), and gives an example. This clearly differentiates from sibling tools like search_diseases (search by name) and lookup_phenotype (phenotype lookup).

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 exact MONDO ID lookups via the example, but does not explicitly state when to use this tool vs alternatives (e.g., search_diseases for name-based search). No guidance on when not to use it or required 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?

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, which cover core behavioral expectations (safe, idempotent, open-ended results). The description adds useful context about returned content (phenotype label, definition, diseases, parent terms) and an approximate result size. However, it does not disclose any potential limitations, rate limits, or authentication requirements, leaving the agent to infer from annotations alone.

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: three sentences of core info plus a concrete example. No redundant or vague language. It front-loads the purpose and then lists return fields in a clear bullet-like manner. Every sentence earns its place.

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

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

Given that the tool has an output schema (not shown but indicated), the description need not detail return format. It covers the main return categories (phenotype label, definition, synonyms, diseases, parent terms) and provides an illustrative example. However, it omits error handling (invalid HPO ID) or pagination details for disease_limit, which would enhance completeness 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?

The input schema provides descriptions for all three parameters (hpo_id, disease_limit, include_diseases) with types, defaults, and constraints. Since schema description coverage is effectively 100%, the description adds no extra semantic value for parameters beyond the example usage. Per guidelines, baseline is 3, and the description does not enhance beyond that.

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 action ('Retrieve an HPO phenotype term') and the resource ('with associated disease annotations'), and lists specific return fields. The example with a concrete HPO ID and expected result ('~400 associated diseases') solidifies understanding. It naturally distinguishes itself from sibling tools like lookup_disease or search_diseases.

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 when you have an HPO ID and want phenotype details plus associated diseases. However, it provides no explicit guidance on when to choose this tool over alternatives (e.g., lookup_disease, search_diseases), nor does it mention prerequisites or exclusion criteria. The default disease_limit and parameters hint at typical use, but direct comparison is absent.

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 provide readOnlyHint, idempotentHint, openWorldHint. The description adds that it combines Open Targets, ChEMBL, and MONDO, enriching understanding of data sources and behavior. No contradictions.

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 well-structured sentences: purpose, output, use cases. No unnecessary words, front-loaded 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 presence of an output schema, the description adequately covers the tool's function and use cases. It could mention limitations or more precise data freshness, but overall it is complete for an agent to decide usage.

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%, meaning the only parameter description is 'MONDO disease ID.' The tool description does not elaborate on the parameter beyond that, failing to add value beyond the schema for a single parameter.

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

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

The description clearly states the tool maps the complete therapeutic landscape for a disease, specifying outputs (approved drugs, pipeline agents, druggable targets, investability summary) and input (MONDO disease ID). This distinguishes it from siblings like get_disease_targets.

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 mentions the tool is 'used in business development, competitive intelligence, and R&D portfolio decisions,' providing context. However, it lacks explicit guidance on when not to use it or comparison to alternatives.

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

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

Annotations indicate readOnlyHint, openWorldHint, idempotentHint. Description adds behavioral context: pipeline steps, resolution from phenotype to diseases to targets, and that it returns UniProt IDs for AlphaFold retrieval. Consistent with annotations. Could mention potential time/network dependency but not required.

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

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

Reasonably concise with bullet points and an example. No wasted sentences, but could be slightly tightened (e.g., combine first two lines). Still very usable.

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 (multi-step pipeline, three parameters, output schema exists), the description explains the complete workflow, provides an example, and references a follow-up tool. Output details are covered by output schema. No gaps.

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

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

Schema descriptions already cover all parameters (hpo_id format, disease_limit and targets_per_disease ranges). Description adds value by showing how parameters fit into the pipeline and provides a concrete example, making the interaction understandable beyond 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?

Clearly states the tool maps a clinical phenotype to protein structures of disease targets. The pipeline explanation (HPO → diseases → targets → UniProt IDs) makes the specific verb and resource unambiguous. Distinguished from siblings by its multi-step mapping nature.

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 an example and mentions using the returned UniProt IDs with analyze_structural_confidence, but does not explicitly state when to use this tool versus alternatives like lookup_disease or get_disease_targets. No when-not-to-use guidance for the many sibling tools.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true. Description adds that no upstream API is called, describes underlying SQLite store, and explains that empty results are common when only boot seed is loaded, providing useful behavioral context beyond annotations.

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 well-structured with clear purpose first, then behavior, then usage notes. Every sentence adds value, no redundancy. Length is appropriate for the tool's complexity.

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 (implied), the description covers all necessary context: local-only nature, storage source, filter semantics, and interpretation of empty results. Completely adequate for an 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?

Schema already has descriptive parameter descriptions (e.g., min/max for limit, enum for druggability_tier). Description adds semantics about AND combination of filters and the meaning of omitting filters, which is not in schema. Slightly above baseline of 3 due to added filter logic insight.

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?

Description clearly states it recalls proteins from a local knowledge graph, uses specific verb 'recall' and resource 'proteins already stored'. Distinguishes from live-lookup siblings by naming alternatives and contrasting local vs live.

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?

Explicitly states when to use (local recall) and when not (for proteins not yet stored), provides alternatives (assess_target_druggability, analyze_structural_confidence), and explains filter combination behavior and interpretation of empty results.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds context about the data source (local SQLite knowledge graph) and that analysis tools do not write to it, which is helpful beyond annotations.

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 three sentences, front-loaded with the core purpose, and every sentence provides useful information without repetition.

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

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

Given the read-only nature, detailed schema, and existence of an output schema, the description sufficiently explains what the tool does and its data source. No gaps remain 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?

The input schema already provides detailed descriptions for each parameter (e.g., gene, tier, limit). The tool description does not add any parameter information, so it adds no 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 searches for stored variants in a local knowledge graph, using a specific verb and resource. It distinguishes itself from sibling tools like lookup_disease or search_diseases by focusing on variants and local 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 explains that no upstream API calls are made and data is served from a local SQLite graph, implying it's for quick local queries. However, it does not explicitly state when to use this tool versus alternatives 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and openWorldHint=true. The description adds context about the mapping purpose and provides an example, but does not disclose any additional behavioral traits such as error behavior or rate limits. With strong annotations, the description is adequate.

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 4 sentences, starting with the core purpose, followed by context and an example. It is appropriately sized with no superfluous text, though the example could be more compactly integrated.

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 presence of an output schema (not shown but indicated as true), the description does not need to explain return values. It covers purpose, integration context, and usage example. It lacks information about error handling or code format validation, but for a simple mapping tool with annotations, it is sufficiently complete.

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

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

The input schema already includes a description for 'icd10_code' with format and example values. The description reinforces this with an example call but does not add new meaning beyond the schema. Schema coverage is effectively high due to the inline 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 action ('Resolve'), the source resource ('ICD-10 clinical code'), and the target resource ('MONDO disease ontology terms'). It distinguishes itself from sibling tools which focus on other disease/target/phenotype operations like 'analyze_structural_confidence' or 'search_diseases'.

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 explains the context of use: 'integration between clinical / EHR data (which uses ICD-10) and the research-grade MONDO ontology'. It does not explicitly state when not to use or mention alternatives, but the sibling list provides implicit differentiation.

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 annotations (readOnlyHint, idempotentHint), the description elaborates on the detection algorithm (centroid distance, greedy clustering, pLDDT threshold), output metrics (radius of gyration, centroid offset, druggability index), and limitations. This provides comprehensive behavioral 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 well-structured with a clear first sentence, followed by algorithm details, output metrics, and caveats. It is concise (~150 words) but packs essential information. Minor improvements could include front-loading the output metrics more directly.

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 and presence of an output schema, the description covers the algorithm, output metrics, limitations, and ideal use cases. It fully enables an agent to understand the tool's behavior and output 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 does not explicitly explain the parameters 'uniprot_id' or 'min_pocket_residues', though the latter is mentioned in the algorithm. With schema description coverage at 0%, the description could provide more parameter context, but the schema itself includes minimal descriptions. The tool's algorithm description indirectly clarifies 'min_pocket_residues' 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 explicitly states the tool's purpose: 'Identify and score putative binding pockets from AlphaFold geometry.' It details the heuristic method and distinguishes itself from validated detectors like fpocket or P2Rank, ensuring no confusion with sibling tools.

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

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

The description clearly specifies when to use this tool ('fast, dependency-free pre-screen') and when not to ('not a substitute for a validated pocket detector'). It also highlights ideal use cases: air-gapped deployments and scenarios needing reproducibility from AlphaFold coordinates.

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 declare readOnlyHint=true, idempotentHint=true, openWorldHint=true. Description adds that results are ranked and include MONDO IDs and cross-references, providing useful behavioral context beyond annotations.

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

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

Three sentences and an example, no wasted words. Front-loaded with purpose, then use case, then example. Highly 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?

For a simple search tool with two well-described parameters and an output schema (implied), the description explains return format (ranked list, MONDO IDs, cross-references) and use case, making it fully 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?

Input schema descriptions cover both parameters fully (query and limit), but the description adds an example call with typical values, which aids understanding. Schema coverage is high, so baseline is 3; the example provides additional value.

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 the tool searches for diseases by name or keyword using the MONDO ontology, distinguishing it from lookup_disease which likely requires an exact ID. The verb 'search' and resource 'diseases (MONDO)' are specific.

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 use case: 'resolving a clinical term to a canonical identifier before querying targets or phenotypes.' Does not explicitly state when not to use or list alternatives, but context is clear.

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 it is a read-only compilation tool that does not add scientific judgement. Annotations confirm readOnlyHint=true. Adds context about data sources and assembly, though no details on rate limits or permissions.

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

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

Two concise sentences followed by a clarifying statement. Front-loaded with action and scope, no redundant 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?

Covers output structure (types of data included) and has output schema. Input requirements are in schema. Missing mention of optional depth parameter but acceptable given schema presence.

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?

Tool description does not elaborate on parameters; relies on schema descriptions. Given 0% schema description coverage, the description should compensate but does not mention uniprot_id, gene_symbol, or depth beyond general dossier concept.

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?

Description clearly states it generates a complete protein intelligence dossier from 7 data sources, specifying the types of data assembled. Distinguishes from siblings like assess_target_druggability by noting it composes upstream databases without scientific judgement.

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?

Implied usage for gathering comprehensive protein data, but no explicit when-to-use or when-not-to-use guidance. Sibling tools exist for analysis, but no direct comparison or exclusion criteria 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?

Annotations (readOnlyHint, idempotentHint, openWorldHint) are consistent with a read-only analysis tool. The description adds transparency by disclosing that specific fields (alphamissense_score, alphamissense_interpretation) are always null/not available, and that several features are roadmap items (not implemented). No contradiction with annotations.

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 bullet points and clear sections, front-loading the purpose. It is longer than minimal but every sentence adds value by specifying limitations and cross-referencing siblings. Could be slightly more concise, but overall good.

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 (multi-source triage) and the presence of an output schema, the description is complete. It explains what the tool does, what it does not do, what it returns (pathogenicity_tier), and provides an example. No significant gaps for an agent to misinterpret.

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

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

The input schema already provides comprehensive descriptions for all parameters. The tool description adds some contextual background (e.g., why include_structure points to structural analysis), but the schema descriptions are sufficient. Given the schema descriptions are detailed, the description's added value for parameter semantics is minimal, so baseline score of 3 is appropriate.

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

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

The description clearly states it performs 'comprehensive clinical triage for a missense variant' and lists the fused components (pathogenicity, population genetics, disease associations, structural context). It distinguishes from sibling tools by explicitly noting that AlphaMissense scores are not available here and directing to generate_variant_clinical_report for that, and to analyze_structural_confidence for structural analysis.

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 and when-not-to-use guidance: mentions that AlphaMissense is not wired and directs to generate_variant_clinical_report, notes that per-variant allele frequencies are not included, and points to analyze_structural_confidence for structural context. Also notes roadmap items that are not yet available.

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