fluxforge

by ali-kishk

Overview Schema Related Servers Score Discussions

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Server Quality Checklist

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Latest release: v2.8.2
Disambiguation5/5
Tools are clearly grouped by domain (boolean, literature, metabolic, etc.) with distinct purposes. Each tool has a unique role and no overlapping functionality is evident.
Naming Consistency5/5
All tools follow a consistent domain.tool_name snake_case naming convention, with verb_noun or verb_verb patterns that are predictable and readable.
Tool Count5/5
18 tools is appropriate for the complex multi-domain pipeline covering Boolean networks, literature, metabolic modeling, PPI, and visualization. Each tool earns its place.
Completeness5/5
The tool set covers the full pipeline from session initialization through literature extraction, constraint compilation, modeling, integration, and visualization, with crucial operations like identifier mapping and convergence checks.
Average 3.2/5 across 18 of 18 tools scored. Lowest: 1.3/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 5 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under Apache 2.0.
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

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It only mentions 'soft flux constraints' and 'provenance-independent comparator agreement' without explaining side effects, mutability, or authorization needs. The agent cannot infer whether this tool is read-only or modifies state.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, but it is cryptic and overloaded with jargon ('gate on provenance-independent comparator agreement'). It is not front-loaded with a clear high-level purpose, and each word does not contribute to clarity for an AI agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 5 parameters, no annotations, no output schema, and no parameter descriptions, the description is wholly inadequate. It omits return values, side effects, and parameter semantics, leaving the agent with no usable context to invoke the tool 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 does not clarify any of the 5 parameters (session_id, validated_output_nodes, compilation_rules, external_comparators, hypothesis_classes). The agent receives no help in understanding what each parameter means or how to provide values.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses domain-specific language ('couple validated nodes as soft flux constraints') but doesn't clearly state what the tool does or its output. The tool name includes 'and_test', yet the description omits 'test' entirely, making the purpose ambiguous. Without distinguishing from siblings like metabolic.simulate or boolean.build_and_test, an AI agent would struggle to determine when this tool is appropriate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. There are no prerequisites, exclusions, or context about the type of task it solves. The description fails to mention any conditions for invocation.
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?
With no annotations, the description carries full burden. It only discloses one behavioral trait (never routing through currency metabolites), omitting mutability, permissions, or side effects. This is insufficient for safe agent invocation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
While the single sentence is brief, it sacrifices necessary detail. It is under-informative and does not earn its place by clarifying key aspects.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (4 parameters, no output schema), the description is severely incomplete. It fails to explain inputs, outputs, or behavioral context, leaving the agent without essential usage information.
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 description provides no explanation of parameters like 'session_id', 'anchor_reactions', 'currency_exclusion_list', or 'max_hops'. The agent receives no semantic help beyond names.
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 verb 'Extract' and resource 'localized metabolic subnetwork', with a specific constraint about currency metabolites. It differentiates from sibling tools like 'metabolic.couple_and_test' and 'metabolic.simulate', but does not explicitly contrast them.
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 provided on when to use this tool versus alternatives, nor any prerequisites or exclusions. The description lacks context for selection.
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?
No annotations are provided, and the description lacks behavioral details such as side effects, required permissions, or performance impacts. It only states the core action without elaboration.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence, but it is too short and lacks structure. It could be expanded to include key details without losing conciseness.
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 no output schema, low schema coverage, and no annotations, the description fails to provide essential context about return values, prerequisites, or typical usage patterns.
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 input schema has 3 parameters but 0% description coverage. The description does not explain what session_id, subgraph_edges, or subgraph_nodes mean, leaving the agent with only parameter names.
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 compiles a subgraph into Boolean rules and finds baseline attractors. It uses specific verbs and resources, and distinguishes from siblings like boolean.augment_compare.
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 explicit guidance on when to use this tool versus alternatives. The context signals and sibling names suggest related tools, but the description provides no such comparison.
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?
With no annotations provided, the description carries the full burden. It only says the tool is a 'fail-closed positive-control gate' and checks structural capability. It does not explain what 'fail-closed' entails (e.g., returns false on failure?), what happens if the GEM is not capable, or any side effects. The behavioral disclosure is minimal.
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 with no redundancies, effectively front-loaded. However, being too concise sacrifices necessary detail. The sentence earns its place but leaves critical gaps.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given three undocumented parameters, no output schema, and no annotations, the description is grossly incomplete. It does not specify return values, error conditions, or how to interpret results. A tool of this complexity demands far more context.
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 input schema has three required parameters with 0% description coverage. The description explains none of them: 'session_id', 'target_objective', and 'positive_control_genes' remain completely undefined. The agent cannot infer their meaning or format from the description.
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 'Fail-closed positive-control gate: is the GEM structurally capable of detecting a known dependency?' clearly states the tool's function (checking GEM capability) and uses domain-specific terminology that distinguishes it from siblings like 'metabolic.simulate' or 'metabolic.couple_and_test'. However, the jargon 'fail-closed positive-control gate' may be opaque to some AI agents, slightly reducing clarity.
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 versus alternatives, nor does it mention prerequisites or exclusions. For example, there is no indication that this tool is a safety check before other analyses, or that it should not be used for validation beyond positive controls.
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?
No annotations exist, so the description must fully disclose behavior. It mentions adding edges and re-simulating but does not clarify if the operation is destructive, requires specific permissions, or how simulation state is managed. The return values are listed but not explained.
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 of 20 words, efficiently front-loading the main action and outcomes. It avoids redundancy but could be rephrased for better readability.
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?
With 4 parameters, no output schema, and technical complexity, the description is incomplete. It mentions two return values without format or interpretation, and important aspects like error handling, edge cases, or performance are absent.
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%, so the description should explain parameters. It implicitly references llm_edges and baseline_signature but does not clarify session_id or permuted_null. The parameter role is partially inferred but not explicitly stated.
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 states the tool adds LLM edges, re-simulates, and gates values, with specific return metrics. It uses clear verbs and identifies resources (edges, simulation). However, jargon like 'gate llm_adds_value' and 'attractor_distance' may not be universally understood, slightly reducing clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
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 versus sibling tools (e.g., boolean.build_and_test, integration.convergence_check). It lacks context about prerequisites, alternatives, or scenarios where this tool is appropriate.
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?
With no annotations, the description must disclose behavioral traits, but it only states the action without mentioning whether it is read-only, destructive, or any other side effects. Key details like graph size limits or permissions are omitted.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence, but it lacks structure and front-loaded details for a 4-parameter tool. It is not overly verbose, but brevity compromises informativeness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (4 params, 0% schema coverage, no output schema, no annotations), the description is severely incomplete. It fails to explain inputs, outputs, or usage context.
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 coverage is 0%, so the description must compensate, but it only references 'seed nodes' without defining format or constraints. Parameters like 'session_id', 'max_hops', and 'resolved_edges' are not explained at all.
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 'extract' and the resource 'signed regulatory subgraph' with specific context 'around seed nodes' and 'representative disease-map module', effectively distinguishing it from sibling tools like metabolic.neighbourhood_extract.
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 provided on when to use this tool versus alternatives, nor are any prerequisites or exclusions mentioned. The agent must infer usage from the name alone.
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?
No annotations are provided, and the description does not disclose behavioral traits such as side effects, required permissions, or performance implications. It only states what is rendered without addressing how the tool behaves under various conditions.
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, well-structured sentence that conveys the core purpose without extraneous words, making it highly efficient for an agent to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (4 parameters, no output schema, no annotations), the description is severely incomplete. It omits parameter semantics, return values, and operational constraints, leaving the agent with insufficient information to invoke the tool 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?
With 0% schema description coverage, the description fails to explain any of the four parameters (ppi_edges, out_path, gpr_edges, session_id). Their roles and formats are entirely undocumented, forcing the agent to guess or rely on external knowledge.
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 renders a '3-layer viral->enzyme->reaction interactive HTML' with 'ipTM tiers and flux overlay', providing a specific verb and resource. It differentiates from siblings like viz.render_metabolic_map and viz.render_subnetwork by its focus on PPI-metabolic integration.
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. It does not mention prerequisites, limitations, or appropriate contexts for usage.
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?
No annotations are provided, so the description carries full burden. It mentions 'interactive HTML' and 'LLM-added edges highlighted' but fails to disclose whether it writes files (hinted by optional out_path), requires authentication, or has side effects. The read-only assumption is implicit, not explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that front-loads the core functionality. However, given the tool's complexity (6 parameters, no output schema), it sacrifices necessary detail for brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 6 parameters, no output schema, and no behavioral annotations, the description is grossly incomplete. It does not explain parameter roles, return value, or how to use the tool effectively.
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 coverage is 0%, and the description provides no details about any of the 6 parameters (including 3 required ones). It adds no meaning beyond their names and types in the schema.
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 states a specific verb ('render') and resource ('regulatory subnetwork with LLM-added edges highlighted'), and it distinguishes from sibling tools like viz.render_metabolic_map and viz.render_ppi_metabolic which focus on metabolic or PPI networks.
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 usage for regulatory subnetworks, providing context, but does not explicitly state when to use or avoid this tool, nor mention alternatives. No guidance on 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.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool is deterministic, lists the decision outcomes, and mentions a 'fail-closed hard stop' for budget_exhausted. However, it does not explain side effects (e.g., session state changes) or authorization needs, which would be expected for a zero-annotation tool.
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 sentence that efficiently conveys the core purpose and outcomes. Every part earns its place—there is no filler or redundancy.
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 complexity of a stopping rule with multiple parameters and no output schema, the description is too high-level. It does not explain how parameters influence the decision, the format of the return value, or how to use the output in a pipeline. This leaves significant gaps for an AI agent to invoke the tool correctly.
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 schema has 6 parameters with 0% description coverage, and the tool description does not explain any parameter meanings (e.g., rounds, min_gain, patience). The description only vaguely references 'per-round augment_compare scores' but does not map to the parameters, leaving the agent to infer from names alone.
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 is a deterministic stopping rule for iterative edge integration, listing the four possible outcomes (continue, converged, noise_indistinguishable, budget_exhausted). It uses a specific verb 'decide' and resource 'stopping rule', making its purpose unambiguous and distinct from sibling tools.
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 does not provide any guidance on when to use this tool versus alternatives, such as when it should be invoked after augment_compare or the prerequisites for running it. No information about when not to use it is given.
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?
No annotations are provided, so the description carries full burden. It mentions 'Boltz-2 structural gate' and classification from ipTM + flux relevance, but does not disclose how the classification works, required input formats, side effects, or 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?
The description is a single sentence, tightly packed with key information about purpose and inputs, with no extraneous words.
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?
With 6 parameters, 3 required, and no output schema, the description lacks details on how to use session_id, expected return values, or the classification logic. Incomplete 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.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%. The description mentions 'ipTM + flux relevance' but does not map clearly to all six parameters (e.g., session_id, bait, prey are unexplained). Only partial meaning is added for iptm and flux-related parameters.
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 'classify', the resource 'bait/prey pair', and the output categories 'physical/regulatory/context_only'. It distinguishes from sibling tools like 'viz.render_ppi_metabolic', which is for visualization, not classification.
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 the tool is for structural classification using ipTM and flux relevance, but does not explicitly provide when-to-use or when-not-to-use criteria, nor mentions 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?
With no annotations provided, the description carries full burden. It discloses that unresolved symbols go through an id-map resolver using multiple sources and that results include authority/confidence/matched_by provenance. This provides good insight into behavior and side effects.
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, front-loading the core purpose. Every word adds value—no redundancy, fluff, or restatement of the tool name. Highly 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 no output schema and zero parameter descriptions, the description is incomplete. It explains the high-level algorithm but fails to document the input parameters (session_id and resolve_unmapped) or the output structure, leaving significant gaps 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 coverage is 0% (no parameter descriptions in schema) and the description does not mention or explain either parameter (session_id or resolve_unmapped). The agent cannot infer how these parameters affect the crosswalk building or resolution process.
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 builds a symbol<->entrez crosswalk between PPI hits and model genes. It specifies the resolution process and provenance details, giving a specific verb ('Build') and resource ('crosswalk'). While not explicitly distinguishing from siblings, the unique combination of PPI hits and model genes differentiates it.
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 usage when reconciling PPI symbols to model gene entrez IDs but provides no explicit guidance on when to use versus alternatives (e.g., metabolic.id_map). It lacks exclusion criteria or when-not scenarios.
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?
No annotations are provided, so the description carries the burden. It mentions loading into a new session but is ambiguous about whether an existing session (via session_id) is reused. Side effects like overwriting data are not addressed.
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 brief with two focused sentences, no unnecessary words. It is front-loaded with the primary action. However, it could include brief mentions of all parameters without much bloat.
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 has 4 parameters, no output schema, and no annotations, the description is insufficient. It does not explain return values, side effects, or the role of data_dir and session_id. This leaves significant gaps for an initialization tool.
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%. The description explains two parameters (disease_context, phenotype_keywords) but omits data_dir and session_id, leaving their purpose unclear. This fails to fully compensate for the missing 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 loads canonical inputs (GEM, PPI, regmap, CRISPR, comparators) with provenance tags into a new session. It specifies the exact resources and action, distinguishing it from sibling tools that perform analysis or visualization.
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 use case: initializing a session with canonical inputs for downstream processes. Optional parameters set disease context and phenotype keywords. However, it does not explicitly state when not to use or provide 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?
No annotations provided, so description must disclose behavior. It describes the DAG execution, return types, and the optional edge-integration loop with self-stopping. Lacks details on side effects, permissions, or resource consumption, but covers key operational modes.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Information is packed into a single dense paragraph. Some redundancy like 'default_visualization=True (default)' could be simplified. Structured bullet points or separate sections would improve readability for a complex tool.
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 14 parameters, no output schema, and no annotations, the description is insufficient. It omits descriptions for most required inputs and doesn't clarify return format beyond output names. A complete description would explain each parameter's role and expected values.
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%, so description must add meaning. It explains iterative_integration, integration_params (with sub-parameters), and default_visualization, but ignores 11 out of 14 parameters (e.g., session_id, positive_control_genes, ppi_edges). Most required params lack any semantic explanation.
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 runs the full fail-closed DAG for a session and returns specific outputs (negative_result_report or target_handoff). It distinguishes itself from siblings by describing iterative vs single-shot modes, which links to integration.convergence_check sibling.
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 guidance on when to use iterative_integration=True vs single-shot, and mentions default_visualization behavior. However, it lacks explicit when-not-to-use scenarios or comparisons with sibling tools like boolean.augment_compare or metabolic.simulate.
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?
No annotations provided, so description carries full burden. It explains the nature of constraints (soft, reversible, no gene deletion) and output types (objective + fluxes vs ranges). Discloses that interaction_constraints apply soft constraints, but does not mention if model is modified or auth requirements.
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?
Three sentences with no redundant information. Front-loads the main verb and resource, then provides constraint and output details efficiently.
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?
With 7 parameters, no output schema, and 1 required param, the description covers the main behavior across methods and constraint types. Could be more explicit about when each method is appropriate, but adequate for a simulation 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?
Schema coverage is 0%, but description adds meaning for key parameters: explains interaction_constraints format, mentions top_n_fluxes and fraction_of_optimum defaults, and connects methods to outputs. However, reaction_list usage is not fully clarified.
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 runs multiple simulation methods (FBA, pFBA, FVA, MOMA) on a metabolic model with optional PPI integration. It distinguishes from sibling tools like metabolic.couple_and_test by specifying the core simulation functionality.
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 usage for metabolic simulations but does not explicitly state when to use this tool versus alternatives like metabolic.couple_and_test. No guidance on method selection criteria 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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Given no annotations, the description does a good job explaining core behavior: overlay constraints on Escher maps and fallback to cytoscape. It also lists the return fields. It lacks details on error handling or permissions, but these are less critical for a visualization tool.
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 sentences) and front-loaded with key functionality. Every sentence adds value without redundancy.
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?
While the core logic and return values are described, the description is incomplete due to lack of parameter explanations and usage examples. Given 6 parameters and no output schema, more detail would be beneficial.
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?
With 0% schema description coverage, the description only mentions 'constraints' indirectly. Parameters like model, title, prefer, out_path, and session_id are not explained, leaving a significant gap.
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 is the default metabolic visualizer that overlays compiled constraints on Escher curated maps, with automatic fallback to cytoscape. This distinguishes it from sibling tools like viz.render_subnetwork.
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?
It positions itself as the default tool and mentions fallback behavior, implicitly guiding when to use it. However, it does not explicitly state when not to use it or name alternatives.
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?
With no annotations, the description fully bears the transparency burden. It elaborates on the deterministic server-side policy: preference order (target, source proxy, drop), role_used tracking, resolution_confidence, and fallback behavior. No contradictions or omissions apparent.
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 relatively long but every sentence contributes meaningful detail (purpose, policy, output properties). It is front-loaded with the main purpose. Minor redundancy could be trimmed, but overall efficient.
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?
Given the complexity (6 params, no annotations, no output schema), the description covers the compilation logic well but leaves gaps: input format for claims, exact output structure, and semantics of several parameters. Adequate but not fully comprehensive.
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 coverage is 0%, yet the description provides little parameter-specific meaning. Only 'claims' is implied by context; other parameters like min_confidence, default_scale_activating, allowed_disease_specificity are not explained. The description fails to compensate for the lack of schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 function: compiling (source,target,sign) triples from literature.extract_claim into InteractionConstraint lists consumed by metabolic.simulate/couple_and_test. It references sibling tools, distinguishing itself as an intermediate compilation step between extraction and simulation.
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 places the tool in a pipeline context: compile after literature.extract_claim, before metabolic.simulate. It details the deterministic policy but does not explicitly state when not to use it or mention alternatives. The context is clear enough for most scenarios.
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?
No annotations are provided, so the description bears full responsibility. It thoroughly discloses the mapping backends (MetaNetX, gene table, MyGene), the output structure ({authority, confidence, matched_by}), and confidence levels. This goes well beyond basic functionality.
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 verbose but efficient, front-loading the main purpose. Each sentence adds value, though it could be slightly more concise without losing information.
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?
The description covers mapping processes and output format but lacks details on input formatting, error handling, and unmappable IDs. Given five parameters and no output schema, the description is moderately complete but has gaps.
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?
With 0% schema description coverage, the description must compensate. While it explains the mapping logic for each entity type, it does not specify input formats (e.g., string formatting, delimiters) or the role of the session_id parameter. This leaves significant ambiguity for the agent.
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 reconciles identifiers to canonical ids for metabolites/reactions (via MetaNetX MNXref) and genes (via Entrez id space). It specifies the verb 'reconcile' and resource 'identifiers to canonical ids', accurately distinguishing it from siblings like namespace.reconcile.
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 explains when to use the tool ('so the pipeline never relies on the LLM guessing an accession') and what it does for different entity types. While it does not explicitly state when not to use or list alternatives, 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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool uses LLM extraction, returns span-grounded claims with provenance and confidence, and that optional parameters are soft signals. This provides sufficient behavioral context for an agent.
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 with no wasted words. The first sentence efficiently states the core purpose and outputs, and the second adds key parameter details. Front-loaded and easy to parse.
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?
The description covers the main extraction purpose, optional parameter roles, and behavioral traits (soft signals). It is fairly complete given no output schema and five parameters, though details on document format or target_entities are missing.
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 description coverage is 0%, but the description adds meaning for disease_context and phenotype_keywords as disambiguation and ranking aids. Other parameters (documents, session_id, target_entities) lack explanation, so the description only partially compensates for the lack of schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 purpose: high-throughput LLM extraction of claims with specific fields (source, target, sign, etc.) and provenance+confidence. It uses specific verbs and outputs, distinguishing it from siblings like literature.compile_constraints.
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 explains that optional parameters disease_context and phenotype_keywords disambiguate references and rank claims, and clarifies they are 'soft signals, never filters.' However, it does not explicitly compare to sibling tools or state when to use this tool versus others.
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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