metadome-link

Server Quality Checklist

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Latest release: v0.3.3
Disambiguation5/5
Each tool has a clearly distinct purpose: compare_positions is for batch comparison, get_position_tolerance for a single residue, get_tolerance_landscape for the full landscape, get_meta_domain for homologous data, etc. There is no ambiguity between tools; they cover different aspects of tolerance landscape analysis.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern using snake_case (e.g., compare_positions, get_position_tolerance, resolve_transcript). The verbs vary appropriately (get, compare, request, resolve, summarize) and nouns are descriptive. No mixing of conventions.
Tool Count5/5
With 11 tools, the server is well-scoped for its domain of MetaDome tolerance landscapes. Each tool serves a necessary function in the workflow (transcript resolution, landscape building, data retrieval, analysis), and the number feels appropriate without being overwhelming or sparse.
Completeness5/5
The tool surface covers the full lifecycle of working with tolerance landscapes: resolving transcripts, requesting and polling builds, retrieving landscapes, accessing per-residue details, comparisons, variant counts, domain info, and summarization. Diagnostics and capabilities tools round out the set. There are no obvious gaps.
Average 4.3/5 across 11 of 11 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 81 commits in the last 12 weeks
- Last stable release on August 10, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and non-destructive. The description adds valuable context: it is a local health check that does not call MetaDome, and lists what data it reports. 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, dense sentence with a clear purpose, followed by a usage statement and signature. Every word adds value, no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple parameter and no output schema, the description covers purpose, usage, and behavioral context well. It could mention return format, but the listed items suffice for a diagnostic tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema covers the single parameter fully with enum and default. The description only repeats the signature, adding no new meaning beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool reports local runtime health without calling MetaDome, listing specific outputs. It distinguishes itself from sibling tools by emphasizing 'WITHOUT calling MetaDome,' though it does not explicitly compare to others like get_server_capabilities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear use case: 'Use this to confirm cache state or diagnose a misconfigured server.' However, it does not mention when not to use or mention alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, destructiveHint. Description adds useful context: required prior call and details of returned fields (ID, Name, residues, meta-domain, depth). 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Reasonably concise: two sentences plus signature. Front-loaded with purpose. Signature is somewhat redundant but helpful.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Missing output schema, but description lists expected output fields. Requires built landscape is clearly stated. For a simple list tool, this is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for both parameters. Description adds little beyond the schema: repeats transcript_id guidance and gives signature but no additional semantic value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clear verb 'List' and specific resource 'Pfam protein domains on a transcript's tolerance landscape'. Distinguishes from siblings like get_meta_domain or summarize_intolerant_regions by explicitly linking to tolerance landscape.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states prerequisite: 'Requires a built landscape (call request_tolerance_landscape first)'. Provides a signature example. Could be improved by noting when not to use or mentioning alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is clear. The description adds value by disclosing that MetaDome lacks true residue-level gnomAD counts (marked as unavailable, not zero) and that ClinVar variants include NCBI URLs. This contextual behavior is not captured by annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: action first, then key caveat, then scope and output details, ending with signature. Every sentence provides essential information without 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.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 8 parameters, 1 required, 100% schema coverage, and no output schema, the description is nearly complete. It covers return type, filtering, positional range handling, pagination, and a notable data limitation. The only gap is a detailed output structure, but the description gives sufficient high-level understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds meaning by explaining the positional parameters (single, range, or whole protein) and mentioning pagination via limit/offset, which clarifies usage beyond the schema. The signature listing reinforces parameter roles but adds minimal new info.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns residue-level ClinVar annotations and Pfam homolog aggregates on a built landscape, with specific verb 'Return' and resource identification. It distinguishes from siblings by detailing the scope (single position, range, or whole protein) and filtering by source, though sibling differentiation is implicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides implied usage (e.g., for residue-level variant counts) and notes a caveat (gnomAD count unavailable), but lacks explicit guidance on when to use this tool versus alternatives like compare_positions or get_position_tolerance. No exclusions or comparative context is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. The description adds behavioral details: the algorithm uses consecutive residues, ranking is ascending by mean sw_dn_ds, and output includes Pfam domains and variant evidence. No contradictions with annotations. However, it does not disclose performance characteristics or edge cases.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise: two short paragraphs. The first covers purpose and algorithm, the second provides the function signature with defaults. No unnecessary words or repetition. The structure is clean and front-loaded with the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (5 params, no output schema), the description adequately explains what the tool does, how it works, and what the output contains (regions with Pfam and variant evidence). It also references resolve_transcript for obtaining transcript_id. However, it does not describe the exact output format or structure, which could be helpful for an agent to parse results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All 5 parameters are fully described in the schema (100% coverage). The description adds value by summarizing the default values in the signature line and providing context on what 'threshold' means (identifies most constrained positions) and how ranking works. This goes beyond just repeating schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool returns 'top ranked contiguous intolerant regions' with specific criteria (sw_dn_ds below threshold, length >= min_run) and ranking (by mean sw_dn_ds ascending). It mentions annotation with Pfam domains and variant evidence, and provides a signature with default values. This clearly distinguishes it from siblings like get_tolerance_landscape or get_position_tolerance.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use when one needs contiguous intolerant regions rather than individual positions, but it does not explicitly state when to use this tool over alternatives or when not to use it. No mention of when to prefer sibling tools like get_tolerance_landscape. Guidance is implicit but not explicit enough for an agent to confidently decide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true and idempotentHint=true. Description adds behavioral details: specific error types (invalid_input, not_found) and the dependency on a built landscape, which annotations do not cover. No contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences plus a concise signature. No wasted words; key information front-loaded. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Description summarizes main outputs but lacks details on return structure, especially for variant evidence and response_mode modes. Without output schema, more specificity would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% description coverage for all 3 parameters. Description provides the signature and mentions response_mode verbosity, but adds little beyond schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Return' and resource: one residue's missense tolerance, domain membership, and variant evidence. It distinguishes from siblings like get_tolerance_landscape by specifying 'on a built tolerance landscape'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly describes error conditions for out-of-range positions and not-yet-built landscapes, guiding the agent to call request_tolerance_landscape first. Could contrast more with siblings but provides clear context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate safe, read-only, idempotent behavior. Description adds valuable context: what is returned, the effect of detail='full', and the signature. 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences cover purpose, usage, and signature. Front-loaded and no fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given simple parameter set, comprehensive annotations, and no output schema, the description fully covers capability discovery needs. No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with enums and descriptions. Description adds minimal new info beyond the schema (e.g., 'detail='full' adds semantics + notes' is similar to schema description). Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns the 'metadome-link discovery surface' with a specific list of items. It distinguishes from siblings like get_diagnostics and get_meta_domain which focus on specific domains or variants.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Call this first in a cold session' and mentions alternative methods (reading metadome:// URIs). Provides signature hints. Room for more explicit when-not-to-use but sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, indicating safe, idempotent reads. The description adds specific behavioral context beyond annotations: batch size cap, per-item error handling for out-of-range positions, and that the whole batch never fails. 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: two sentences plus a signature line. Every sentence adds value—main purpose, error handling behavior, and a clear function signature. No redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (batch comparison with error handling) and lack of output schema, the description reasonably covers key behaviors: return type (tolerance table with specific columns like sw_dn_ds, ref_aa, domain ids), error handling, batch cap, and parameters. It could elaborate slightly on the return structure, but the included column hints are sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for all three parameters. The description adds a signature line that mirrors the schema but does not provide additional semantic meaning beyond what is already in the schema. For example, the batch behavior is mentioned in the description but not tied specifically to the positions parameter beyond schema text.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description specifies a clear verb ('Return') and resource ('side-by-side tolerance table ... for a batch of residue positions on a built landscape'). It distinguishes from sibling tools like get_position_tolerance by emphasizing batch processing and side-by-side comparison. Includes details about error handling and batch size cap, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use this tool (for batch comparison of positions) and mentions behavioral safeguards ('batch never fails for one bad position'). It does not explicitly list alternative sibling tools for single-position queries, but the context of batch side-by-side comparison effectively differentiates from get_position_tolerance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses behavioral traits beyond annotations: it returns a poll handle, details status='ready' vs 'processing' with up to ~1 hour cold build, explains polling with poll_after_s, and declares idempotency. This significantly aids agent understanding. 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences plus a compact signature line. It is front-loaded with the core action and resource, and every sentence adds value. No redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's 2 parameters, lack of output schema, and presence of annotations, the description provides sufficient context: it explains the asynchronous nature, statuses, polling, and idempotency. The absence of an output schema description is a minor gap, but the poll handle is adequately explained.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description mentions the parameters in the signature line but adds no additional meaning beyond what the schema provides. No new semantic details about values or constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Submit (or re-confirm)', the resource 'MetaDome tolerance-landscape build', and the context 'for a versioned transcript'. It distinguishes from siblings like get_tolerance_landscape and resolve_transcript by specifying the action and return of a poll handle.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use the tool (to get a landscape build), what to expect (status flags), and the follow-up action (poll get_tolerance_landscape). It also notes idempotency. It lacks explicit when-not-to-use instructions but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations (readOnlyHint, idempotentHint, non-destructive) are consistent. Description adds key behavioral details: returns empty lists for missing meta-domain (not an error), requires built landscape, and includes homolog gene_name in output. 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single well-structured paragraph. Begins with core action, then explains behavior, then gives signature. Every sentence adds meaningful information. No repetition or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 6 parameters, no output schema, and annotations present, the description explains return type, error handling, input derivation, and requirement. Lacks explicit pagination description but limit/offset are in schema. Sufficient for an AI agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, but description adds extra meaning: clarifies 'homologous' and 'aligned consensus position' for position parameter, explains derivation behavior for domains parameter. Provides value beyond schema, especially for domains and response_mode.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it returns homologous variant evidence for a residue, specifying data sources (gnomAD, ClinVar) and fields (homolog gene_name). Contrasts with omitting domains to auto-derive from cached mapping. Distinguishes from sibling tools like get_position_tolerance and get_variant_counts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit usage context: requires built landscape, empty lists for no meta-domain, option to omit domains for auto-derivation. Does not explicitly compare to alternatives among siblings or state when not to use, but the context is sufficient for an AI agent to decide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive. The description adds value by disclosing cache-first behavior, the possibility of returning a 'processing' status instead of an error, and the need to poll after poll_after_s. It also explains the inclusive nature of position range slicing.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (about 4 sentences) and well-structured: what the tool returns, optional parameters, behavior when processing, and a signature example. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 6 parameters and no output schema, the description covers the main aspects: return data (Pfam domains and per-residue annotations), parameter usage (slicing, pagination), and important behavior (cache-first, processing status). It could elaborate on the exact output structure, but the conceptual description is sufficient for most use cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear parameter descriptions. The description adds context by emphasizing the inclusive residue range for position_start/position_stop and stating that limit/offset are for pagination. While not essential, it reinforces parameter relationships.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states it returns the MetaDome tolerance landscape for a built transcript, including Pfam domains and per-residue annotations. It also mentions cache-first behavior and the optional slicing by residue range, making the purpose very specific and distinguishable from siblings like request_tolerance_landscape.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly indicates when to use this tool: to get the tolerance landscape if available, and if the build is still running, it returns a processing status to poll again. It implicitly suggests using request_tolerance_landscape if the build has not started, but does not explicitly state this alternative, which would improve the score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds significant behavioral context beyond annotations: it specifies resolution to GRCh37, sorting by protein length, flagging longest as canonical, validation of bare ENST id, and response mode verbosity. Annotations already indicate readOnlyHint and idempotentHint, but the description enriches understanding of the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with four sentences, each adding distinct value: main action, input details, suggested follow-up, and signature. It is front-loaded and no sentences are redundant or wasteful.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity and lack of output schema, the description covers input types, processing details, response modes, and a clear usage hint for downstream tools. It is fully adequate for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description adds value by elaborating on the query parameter's behavior (gene symbol vs ENST id processing) and the signature line explicitly shows parameter names. It slightly exceeds the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it resolves a gene symbol or versioned Ensembl transcript ID to MetaDome GRCh37 transcript candidates. It distinguishes between input types (gene symbol vs bare ENST id) and mentions the canonical transcript flag. This is a specific verb+resource action with clear differentiation from siblings like request_tolerance_landscape.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit context for when to use the tool (to get transcript candidates and canonical transcript) and suggests a follow-up tool (request_tolerance_landscape). It does not explicitly state when not to use it, but the context is sufficient for an agent to decide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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