CRC-LNM Medical Agent

Server Quality Checklist

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Latest release: v1.0.10
Disambiguation5/5
Each tool serves a distinct pipeline stage: model info retrieval, case data QC, CT feature preparation, pathology feature preparation, multimodal prediction, and report generation. No two tools appear to handle the same responsibility, so an agent can unambiguously select the right tool for each step.
Naming Consistency4/5
All tools share the consistent 'crc_lnm_' prefix and use snake_case. Most follow a verb_noun pattern (get_model_info, prepare_ct_features, generate_report), though 'case_data_qc' is more noun-like and 'predict_multimodal' uses an adjective, creating minor deviations. Overall the naming is predictable and readable.
Tool Count5/5
Six tools cover the full end-to-end workflow of a specialized medical AI pipeline without redundancy. The count is appropriately scoped for the server's purpose, neither sparse nor bloated.
Completeness5/5
The tool set covers the complete workflow from model inspection and data QC through feature preparation, prediction, and report generation. There are no obvious gaps for the intended use case, as each step in the pipeline is represented.
Average 3/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 6 commits in the last 12 weeks
- Last stable release on August 7, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations indicating safety hints, the description carries the full burden of behavioral disclosure. It says 'validate and retain,' implying a side effect, but does not disclose prerequisites (beyond 'approved'), failure modes, or what 'retain' means operationally. No detail is given about the validation criteria or what happens to rejected features.
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?
The description is a single, tightly-written sentence that is front-loaded with the action. Every word contributes to the meaning, and there is no fluff. However, the brevity sacrifices essential detail, so it is not a 5.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Although the tool has an output schema and sibling tools suggest a multi-step pipeline (QC → prepare → predict → report), the description provides no context on where this tool fits. It does not mention that it is a prerequisite for prediction or that it consumes QC'd artifacts. A minimal complete description should place it in the workflow.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds no parameter-specific meaning. The input schema has five parameters (e.g., qc_artifact_id with a pattern but no description), and the tool description does not explain the role of qc_artifact_id or the contract fields. With 0% schema description coverage, the description should compensate but does not.
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 uses a specific verb ('Validate and retain') and resource ('pathology features') with a precise dimension (768-dimensional), clearly distinguishing it from the sibling tool crc_lnm_prepare_ct_features. However, it does not explicitly contrast with other tools or mention the pipeline context, so it stops short of a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool vs alternatives. It does not mention that this is for pathology (vs CT), nor that it should be run after QC. The schema contains a Chinese note about only accepting QC'd artifacts, but the description itself offers no usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide no read-only, idempotent, or destructive hints, so the description must disclose behavior. It adds 'deterministic' and 'escaped', but these are vague and do not clarify side effects, required permissions, or whether the report is stored or returned. The schema-level Chinese description adds some scope, but the tool description itself is insufficient.
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?
One sentence, front-loaded with the verb and object. It is concise with no filler. The word 'escaped' is slightly ambiguous, but the structure is efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given five required parameters and an output schema, the description is too thin. It lacks prerequisites, input semantics, and behavioral details. The tool appears to be part of a pipeline, but the description alone does not position it within that pipeline.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description mentions no parameters. With schema description coverage at 0%, the description must compensate, but it provides zero explanation of qc_artifact_id, prediction_artifact_id, or the required metadata fields. The schema's patterns and $defs description are useful, but the tool description adds no parameter meaning.
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 "Generate a deterministic escaped research-assistance report" clearly identifies a Generate operation producing a report, with qualifiers "deterministic" and "research-assistance" that distinguish it from sibling tools like prediction, QC, or feature preparation. It is specific and names the resource (report) and its nature.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers no guidance on when to use this tool versus alternatives, nor does it mention prerequisites such as first obtaining QC and prediction artifact IDs. The required parameters imply a pipeline ordering, but the description itself provides no explicit usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description is nearly silent on behavioral traits. It says "locked" and "after compatibility gates," but does not explain side effects, prerequisites, failure modes, or whether the operation is deterministic or state-changing. With all annotations set to false, the description carries the full burden, and it provides almost no useful behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, front-loaded with the action verb, and contains zero filler. It is efficient in structure, though it is quite sparse. Compared to a tautology like "Process," this has substantive content and is appropriately sized for what it conveys.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (multimodal ensemble, five required parameters, nested schemas, and an output schema), the description is far too minimal to provide adequate orientation. It omits prerequisites (e.g., prior QC and feature preparation), the need for same-workflow artifacts, and any guidance on failure modes. The presence of an output schema covers return values, but the overall picture is incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is reported as 0%, so the description must compensate by explaining parameter semantics. It does not mention any of the required parameters (artifact IDs, clinical fields, request IDs, etc.) and adds no meaning beyond the schema. The schema itself contains some descriptions for nested objects, but the tool description is entirely silent on parameters.
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 uses a specific verb "Run" and a clear resource "locked five-member multimodal ensemble," which unambiguously conveys that this tool executes the ensemble prediction model. It also distinguishes itself from sibling tools by implying it is the inference step after data preparation and before report generation, though it does not explicitly say "predict" or contrast with siblings.
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 phrase "after compatibility gates" provides an implied usage condition, indicating the tool should only be run once certain gates have passed. However, it does not name the gates or mention alternatives (e.g., when to use get_model_info or generate_report), and it offers no explicit exclusions or 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide no safety profile (readOnlyHint=false, destructiveHint=false), so the description carries the full burden. It does not disclose whether validation has side effects, what happens on failure, or any specific behavioral traits beyond the generic 'Validate'. This is insufficient for a tool that likely operates on sensitive case data.
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 concise sentence with no redundant words. It is front-loaded with the verb 'Validate' and clearly states the three validation dimensions. Every word serves a purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema, the description omits critical context for a validation tool: it doesn't state the expected inputs beyond the vague 'case', the process, or how results are returned. With 5 required parameters and no schema descriptions, the tool is under-specified for an agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the tool description adds no parameter-level context. It does not explain what 'case_ref', 'input', or other required parameters mean, nor how the 'fallback_policy' and 'ct_source_preference' influence behavior. The description fails to compensate for the absence of 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 clearly states the tool's verb ('Validate') and specific scope ('deidentified case integrity, privacy and required modalities'). This distinguishes it from sibling tools like crc_lnm_predict_multimodal and crc_lnm_prepare_ct_features, which focus on prediction and feature preparation rather than data QC.
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 this tool is a preprocessing/validation step before the modeling pipeline, but it does not explicitly state when to use it relative to the sibling tools. No exclusions or alternative recommendations are provided, leaving the usage context to be inferred from the tool name and sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are all false, so the description must carry the burden. It mentions 'validate and retain' but doesn't disclose side effects, what 'retain' entails, whether it writes output, or any prerequisites.
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?
One sentence with no wasted words. It is concise and front-loaded, though minimal.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 5 parameters, no schema descriptions, and no annotation hints, the description is too brief. It doesn't explain the tool's role in the workflow, prerequisites, or output behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description provides no details about parameters like qc_artifact_id or source. It only vaguely aligns with 'precomputed' but doesn't explain the input structure.
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 uses a specific verb ('validate and retain') and a specific resource ('approved precomputed 1409-dimensional CT features'), clearly distinguishing it from siblings like pathology features or prediction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives. It doesn't mention that it's for precomputed CT features only, nor does it reference sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered. The description adds 'integrity-checked', implying the returned metadata is validated, but it does not explain what this check entails or describe any error behavior. This is minimal added 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence stating the tool's purpose without redundancy. Every word contributes to the meaning, making it concise and well-structured.
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?
This is a simple read-only getter and an output schema exists to describe return values. However, the description omits any context about the required parameters and does not explain why integrity-checking matters or how the returned information fits into the broader workflow (e.g., before making predictions). The absence of usage guidance also affects completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 4 required parameters with 0% description coverage. The tool description does not mention contract_version, request_id, trace_id, or the empty input requirement, so it fails to compensate for the lack of parameter explanations. An agent would be left without any guidance on what these parameters mean or how to set them.
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 uses the specific verb 'Return' and identifies the exact content: model dimensions, version, hashes and threshold. It clearly distinguishes this tool from siblings like prediction or feature preparation by focusing on metadata retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus alternatives like crc_lnm_predict_multimodal or crc_lnm_generate_report. The description does not mention any prerequisites, exclusions, or typical workflow placement.
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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