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

Annotations already disclose readOnly, idempotent, and openWorld behavior. Beyond that, the description adds genuinely useful context: it explicitly notes the return is a summary-level reliability report, 'not a per-residue profile', defining output scope, and describes the three analytical components (pLDDT mean + tier, PAE mean/max + domain boundaries, druggability pre-screen).

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

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

Front-loaded with the core action in the first sentence, then a clearly bulleted breakdown of the three outputs. No filler, though the parenthetical pLDDT/PAE expansions sit slightly close to the title's already-redundant 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?

An output schema exists, so return values need not be fully described, yet the description usefully characterizes the summary nature of the output. With annotations covering the safety profile and a single parameter, the definition is nearly complete; the missing input guidance is the main 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 description says nothing about the single input parameter (uniprot_id), providing no format, scope, or isoform guidance. With schema description coverage reported at 0%, the description fails to compensate for the undocumented 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 states a specific verb+resource (analyze AlphaFold structural confidence via pLDDT and PAE) and enumerates the exact outputs, so an agent knows precisely what the tool produces. However, it never distinguishes itself from overlapping siblings such as assess_target_druggability (which the 'druggability pre-screen' clause clearly overlaps) or score_binding_pocket_geometry.

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

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

There is no when-to-use, when-not-to-use, or alternative-tool guidance. The description only says what it analyzes, leaving the agent to infer that this is for assessing confidence in an existing AlphaFold model rather than choosing among the many structural 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?

Annotations already declare readOnlyHint, openWorldHint and idempotentHint, so the safety profile is covered. The description adds genuine value beyond that: it discloses exactly which external sources feed the score and explicitly bounds its authority ('assembles existing public-database evidence into one tier ... not a validated predictive model'), which is important context for an agent interpreting the output.

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

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

Front-loaded with the classification output, then a numbered breakdown of the four signals, then a scope caveat. Every element earns its place, though the four-bullet enumeration is longer than strictly needed to select the 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?

An output schema exists, so return-value documentation is not required. The description is complete enough for an agent to know the tool returns a classification tier built from four sources, and it bounds the reliability appropriately. The only gap is the absence of when-to-use routing against similar sibling tools.

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 contributes nothing about parameters. The nested schema does carry descriptions for both uniprot_id (including format examples) and include_clinical_stage, so the meaning is not lost, but the description adds no syntax, default, or interaction guidance. Baseline 3 is appropriate for a minimally parameterised tool whose schema does the work.

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

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

States a specific verb+resource ('druggability assessment for a protein target') and enumerates the four evidence signals it fuses into a named HOT/WARM/COLD/NOT_DRUGGABLE classification. This distinguishes it clearly from sibling analysis tools like analyze_structural_confidence or score_binding_pocket_geometry, which cover only one signal.

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 (aggregate an overall druggability tier rather than a single-signal read) and disclaims what it is not ('does not add scientific judgement, not a validated predictive model'). However it never states when to choose this over sibling tools such as analyze_structural_confidence or score_binding_pocket_geometry, nor any prerequisites, so routing is left to inference.

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

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

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 already declare readOnlyHint, openWorldHint, and idempotentHint, so safety is covered. The description adds the analytical shape (shared vs unique vs Jaccard), but with an output schema present this largely restates return structure rather than disclosing new behavior like computation method, limits, or provenance.

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

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

Front-loads the verb+resource in the first sentence, then uses a clean 'Returns:' bullet list. The interposed 'a key analysis for drug repurposing...' clause leans slightly toward marketing but still conveys use context.

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 nested schema is well documented and an output schema exists, so the description need not enumerate return fields — yet it does, giving the agent a clear picture of shared/unique/Jaccard outputs. It is broadly complete for a read-only comparison tool.

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

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

The description only refers to 'two diseases' and adds no meaning beyond the schema, which itself documents mondo_id_a (with example), mondo_id_b, and target_limit's 1–50 range. Parameter semantics are adequately covered by the nested schema descriptions, so a baseline 3 is warranted.

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

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

States a specific verb and resource — 'Compare the protein target landscapes of two diseases' — and the sub-bullet clarifies the output construct ('shared and unique targets'). It doesn't explicitly differentiate from similar siblings such as get_common_disease_targets or compare_proteins_topologically, so an agent must infer the distinction.

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

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

The phrase 'a key analysis for drug repurposing, identifying shared mechanisms, and understanding comorbidity' implies when the tool is valuable, but it offers no explicit when-not guidance and does not name the closely related sibling get_common_disease_targets as the alternative. Usage is implied rather than prescribed.

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?

Goes well beyond the readOnly/idempotent annotations by disclosing the optional [tda] extra dependency, the degraded coarse fallback that does NOT compute persistent homology, the flag on results when this happens, and the no-structure result when AlphaFold has no model. It also discloses the tool's epistemic limits (geometry-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?

Front-loaded with the core action, then justified detail. The Betti-number explanation and caveats are substantive rather than filler, though the paragraph is dense and slightly long for a single-parameter 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?

With an output schema present, the description needn't restate return structure, yet it still enumerates what comes back (vector, Betti numbers, residue count, method flag). Dependency, fallback, and failure modes are all covered, leaving no critical gap 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?

Only one parameter, and the schema already carries the accession pattern and format description. The prose adds the semantic role of uniprot_id (drives the AlphaFold model fetch), which is real value, but no extra syntax or edge-case guidance.

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

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

States a precise verb+resource ('Compute a rotation-invariant topological fingerprint of a protein's fold') and specifies the method (Vietoris-Rips persistent homology over the Cα point cloud) and output (64-dim vector plus Betti numbers). It is clearly distinguishable from siblings like get_protein_structure or analyze_structural_confidence.

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 positions the tool as the per-protein input to two named sibling tools and says which downstream tools consume the fingerprints. It also states the boundary condition: 'not a substitute for sequence alignment, RMSD, or functional-homology assessment.'

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

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

Annotations already cover the safety profile (readOnly, idempotent, closed-world), and the description goes well beyond them: it discloses the underlying data source (per-residue AlphaFold pLDDT from PDB C-alpha), the exact threshold (runs below 50), the alternative explanations for low pLDDT, the fact that terminal labels are relative to the modeled fragment, and that numbering is not SIFTS-mapped to UniProt.

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

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

Purpose is front-loaded in the first sentence, which is good. But the body runs to six paragraphs of overlapping disclaimers ('not a validated disorder predictor, biomolecular mechanism classifier, or clinical result'), and the closing citation to Ruff & Pappu does not help an agent decide whether or how to invoke the 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?

For a single-parameter, read-only tool with an output schema, the description supplies everything needed: what it reads, how it thresholds, what it returns (model-local indices, PDB identifiers, heuristic labels), and the interpretive limits of the result. Return values are also covered by the output schema.

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?

One parameter with 0% schema description coverage, so the description must carry the burden. It conveys the input is a UniProt accession and that output numbering is not SIFTS-mapped to full-length UniProt, which clarifies the input/output relationship, but it never restates the accepted accession format or isoform suffix handling that the pattern encodes.

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 opening sentence gives a specific verb and resource ('Flag low-confidence modeled regions as intrinsic-disorder candidates') and the following text pins down the mechanism: pLDDT from PDB C-alpha records, contiguous runs below 50. It does not name or contrast itself with any sibling tool (e.g. analyze_structural_confidence), so it is clear but not sibling-differentiating.

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 rather than stated: the agent can infer this is a screening step for disorder candidates, and the description warns that low pLDDT is NOT independent evidence of disorder and that this is not a validated predictor. However, there is no explicit 'use this when / use X instead' guidance and no named alternative.

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?

With readOnlyHint=true, idempotentHint=true and openWorldHint=true already declared, the description still adds real value: it discloses that the call fans out to seven external databases, that results are evidence aggregation rather than a clinical call, and one explicit negative capability (no 3D structural impact). It stops short of describing cost/latency or partial-failure behavior when an upstream source is unavailable.

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

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

Purpose is front-loaded in sentence one, the safety caveat follows immediately, and the source list and exclusions come last, so the structure is sound. The numbered seven-source block is verbose relative to its decision value, but each entry concretely describes what evidence the report will contain.

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

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

An output schema exists, so return-value detail is unnecessary, and the description covers scope, data provenance, and the clinical-use disclaimer adequately for a nested-input, single-required-parameter tool. The only gap is that the opt-in literature/UniProt path is never mentioned, so an agent could overlook that capability.

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?

Top-level schema description coverage is reported as 0%, so the description carries some burden, and it does clarify that input is a single HGVS variant. But it says nothing about the optional literature flags (include_literature_evidence, literature_uniprot_id, literature_uniprot_residue) or how include_drug_context / include_population_breakdown change output, so it only partially compensates.

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 opening sentence names a specific verb and artifact (generate a multi-source variant interpretation report) and the following text scopes it to a single HGVS variant, distinguishing it from lookup/query siblings. The numbered enumeration of the seven upstream sources makes the tool's scope unmistakable without opening the schema.

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

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

It gives a strong usage caveat ('research aid... not a clinical interpretation... must not be used as a diagnostic without independent review') and a scope exclusion ('does not compute variant-specific three-dimensional structural impact'), which implicitly routes to triage_variant_3d. However, it never explicitly states when to choose this over classify_variant_acmg or query_variant_database, so selection between siblings is still left to inference.

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

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

Annotations already 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 already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true, so safety is covered; the description goes further by enumerating returned fields (entry ID, pLDDT, download URLs), flagging the legacy-only PAE image URL, warning that coordinates are large, and documenting the structure_available:false coverage-gap behavior. That is meaningful context beyond the structured fields.

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

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

Front-loaded with the core purpose and the key routing rule, then returns and edge cases. Every section earns its place, though the field enumeration and the four-sibling list make it longer than strictly necessary for a single-parameter read 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?

With annotations, an output schema, and nested params already present, the remaining burden is edge-case behavior and routing — both covered (structure_available:false, legacy PAE image caveat, non-overlapping sibling scopes). Nothing an agent needs to call this correctly is missing.

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

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

Schema description coverage is reported as 0% at the top level, but the nested parameter descriptions are actually informative; the description reinforces include_coordinates by explaining it embeds the full PDB coordinate text and that it is large. It adds size/behavior guidance beyond a bare boolean, though it does not add anything about uniprot_id format.

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 a specific verb+resource ('Retrieve a protein's AlphaFold model') and explicitly calls itself 'the single entry point for getting the predicted structure itself,' which cleanly separates it from siblings that interpret structures. An agent can identify this as the retrieval tool without opening the schema.

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

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

States the when-to-use rule directly ('use the sibling structure tools for interpretation rather than retrieval') and names four alternatives with their distinct scopes: analyze_structural_confidence, score_binding_pocket_geometry, compute_topology_fingerprint, detect_intrinsically_disordered. This is explicit routing, not inference.

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

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

Annotations already declare readOnly, idempotent, 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?

Annotations already declare read-only, idempotent and closed-world behavior, so the bar is low, and the description clears it by disclosing the actual algorithm (60% inner core, 8 Angstrom greedy growth, pLDDT >= 50 filter), the exact meaning of each returned metric, and the caveat about centroid offset not measuring solvent burial.

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 front-loaded and organized into purpose, method, outputs, and caveats. However, since an output schema exists, the paragraph detailing radius of gyration, centroid offset, mean pLDDT and the four sub-scores partially restates structured return data, making it longer than strictly necessary.

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

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

For a single-parameter, read-only analysis tool with an output schema, the description covers method, thresholds, return-value interpretation, and limitations. Nothing an agent needs to decide to call it, or to interpret the call afterwards, 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?

Schema coverage is reported at 0% for the top-level parameter, so the description carries extra weight. It explains min_pocket_residues operationally ('a cluster is kept when it has at least min_pocket_residues members') and anchors uniprot_id to AlphaFold coordinates, adding role-level meaning beyond the raw JSON schema definitions.

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 first sentence states a specific verb (identify/score), a specific resource (putative binding pockets), and a specific input domain (AlphaFold geometry). An agent can distinguish this from siblings like assess_target_druggability or analyze_structural_confidence without opening the schema.

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

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

Explicitly frames itself as a fast, dependency-free pre-screen and warns it is not a substitute for validated detectors such as fpocket or P2Rank, which is genuine when-to-use guidance. It stops short of naming a sibling tool as the alternative and gives no when-not-to-use conditions tied to this tool family.

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?

Annotations already declare readOnlyHint=true, openWorldHint=true, and idempotentHint=false, so safety and openness are covered. The description adds one genuinely useful behavioral fact — that output is composed, not interpreted — but says nothing about latency/cost of hitting 7 sources, caching, or how the non-idempotent hint manifests.

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

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

Two short paragraphs with the composite nature front-loaded and the boundary condition last. Every sentence carries information; nothing is restated from the title.

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

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

With an output schema present, return values need not be described, and the description covers what is assembled and the read-only, non-judgemental nature. It falls short only on routing guidance among the many sibling tools and on any note about the cost of a 7-source composite call.

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

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

The reported schema coverage is 0%, but the nested schema actually documents all three inner properties well (enum semantics for 'depth', the accession pattern, and why gene_symbol is required). The description adds no parameter-level meaning beyond naming the data categories, so it neither compensates for the reported gap nor exceeds the schema.

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

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

States a specific verb ('Generate') and resource ('protein intelligence dossier'), then enumerates the concrete content it aggregates (disease associations, drug precedent, population constraint, ClinVar variants, orthologs). The closing sentence 'composes upstream databases; it does not add scientific judgement' sharply delimits it from analytical siblings like assess_target_druggability or classify_variant_acmg.

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

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

Usage is implied by 'for one protein into a single structured record', which tells the agent this is a one-protein aggregation call, but there is no explicit when-to-use versus alternatives (e.g., vs. query_protein_database for a single field, or get_gene_phenotype_profile). No exclusions or prerequisites are stated.

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

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

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.