oraclaw-mcp-server

Server Quality Checklist

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Latest release: v1.0.1
Disambiguation5/5
Each tool has a clearly distinct purpose, with descriptions that guide selection among related tools (e.g., optimize_bandit vs. optimize_contextual, analyze_graph vs. plan_pathfind). No two tools appear to overlap in functionality.
Naming Consistency5/5
All tool names follow a consistent `verb_noun` pattern with lowercase and underscores (e.g., `analyze_graph`, `predict_bayesian`, `solve_schedule`). The naming is predictable and clear throughout.
Tool Count5/5
With 17 tools covering optimization, prediction, simulation, risk, graph analysis, scheduling, and constraints, the count is well-scoped for a general-purpose analytical toolkit. Each tool earns its place without overwhelming the surface.
Completeness4/5
The tool surface provides comprehensive coverage for the stated domain (decision support, analytics, optimization). Minor gaps exist, such as missing tools for simple regression or categorical classification, but core workflows like prediction, simulation, and optimization are well-covered.
Average 4.6/5 across 17 of 17 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 80 of 80 community issues answered or closed in the last 6 months
- 24 commits in the last 12 weeks
- Last stable release on June 16, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
View related servers

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 indicate readOnlyHint=true and idempotentHint=true, covering safety and repeatability. The description adds behavioral context: it's premium (implying cost), requires an API key, and computes multiple analytics in one call. No contradictions. A high score as the description adds value beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is four sentences long, front-loaded with the core purpose and key differentiator. Every sentence adds value: purpose, use cases, alternative, prerequisite. No redundant or vague language. Highly efficient.
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 complexity (multiple analytics, premium, sibling tools), the description covers purpose, usage, alternative, and prerequisite. Output schema exists so return values are covered. It doesn't discuss limitations or error handling, but the core information is present. A minor gap in mentioning required parameters explicitly is offset by schema.
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 50% (only sourceGoal and targetGoal have descriptions). The description does not elaborate on nodes or edges beyond mentioning 'directed graph'. It adds no meaning to the node properties (id, type, label, confidence) or edge properties (source, target, type, weight). The description fails to compensate for the low coverage.
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 computes multiple structural properties (PageRank, Louvain, critical path, bottlenecks) and distinguishes it from the sibling tool plan_pathfind by noting the latter is for simple pathfinding only. The verb 'Compute' and specific metrics make the purpose very clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says when to use the tool (for influential nodes, communities, chokepoints) and when not to (for pathfinding alone, use plan_pathfind, which is faster and free). It also mentions the prerequisite ORACLAW_API_KEY. This provides strong usage guidance.
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?
Description adds context beyond annotations (e.g., returns decomposed uncertainty, agreement score, weight share, entropy). Annotations already cover readOnly, destructive, idempotent, openWorld. 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?
Three sentences front-loaded with purpose and output. The word 'Free.' at end is slightly ambiguous but not misleading. Efficient overall.
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?
Covers purpose, usage, output, and differentiation. Has output schema available. Could mention minimum predictions requirement but overall 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%, so baseline 3. Description does not add significant meaning beyond schema; it mentions 'heterogeneous predictors' but doesn't elaborate on parameter details.
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 'Combine N model predictions into a single consensus value' and distinguishes from sibling 'score_convergence' by specifying use case for heterogeneous predictors.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says when to use (fuse outputs from heterogeneous predictors) and provides an alternative: 'For fusing source-agreement on a probability of one event, use score_convergence.'
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=true and destructiveHint=false, so the safety profile is clear. The description adds value by specifying that the tool returns 'variable assignments + objective value', that it is a solver using HiGHS, and that it requires the ORACLAW_API_KEY. This extra context justifies a score above 3 without contradicting any 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 two sentences and a short note. The first sentence defines the core purpose compactly, the second provides usage guidance and alternative tools, and the third mentions returns and requirements. Every sentence adds distinct value with no redundancy or verbose explanations. This is highly efficient.
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 complex optimization tool, the description covers the problem type, optimality guarantee, example domains, and alternative tools. It mentions the return value. However, it omits details about input structure (though schema exists) and does not explain the output schema despite its existence. An agent might need to infer the variable conventions, but the description is largely sufficient for the main scenarios. Could be improved with more param guidance, but overall good.
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 only 25%, meaning most parameters lack schema-level descriptions. The tool description does not compensate: it mentions the type of coefficients but gives no guidance on how to structure direction, objective map, variable list, or constraint objects. For a complex optimization tool, this leaves significant ambiguity for the agent. The use-case examples imply structure but do not directly explain the 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 what the tool does: 'Solve linear / mixed-integer / quadratic programs (HiGHS solver).' It provides specific examples of use cases (budget allocation, supply chain optimization, etc.) and explicitly distinguishes from sibling tools like optimize_cmaes and solve_schedule, leaving no ambiguity about its purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool: 'Use when the objective and constraints are linear (or quadratic) and you need a provably optimal solution.' It also provides clear alternatives: 'For continuous black-box objectives, use optimize_cmaes. For task→slot scheduling, use solve_schedule.' This gives the agent clear decision-making 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?
Adds context beyond annotations by disclosing stochastic nature ('same input gives different best chromosome each run') and that it is 'Free'. Annotations already provide readOnlyHint, so description supplements with behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences: purpose, usage guidelines, and stochastic disclosure. Each sentence adds value without redundancy. Front-loaded with most important information.
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?
Covers purpose, domain, alternatives, and stochasticity. Given output schema exists, return values are not required. Minor gap: explicit description of how to set up multi-objective optimization could be helpful, but phrase 'with optional Pareto frontier' sufficiently hints at it.
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 89%, so baseline is 3. Description does not elaborate on parameters beyond mentioning 'optional Pareto frontier' which is implied behavior, not parameter-specific. Adequate but not additional 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?
The description clearly states it is a 'genetic algorithm for combinatorial / discrete optimization with optional Pareto frontier for multi-objective problems', using specific verb and resource. It distinguishes from sibling optimize_cmaes by domain (discrete vs continuous).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Use when the search space is discrete or mixed' and directs to optimize_cmaes for continuous problems. Also mentions stochastic behavior, providing clear when-to-use and when-not-to-use advice.
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. The description adds value by naming the specific algorithms (A* + Yen's) and explaining heuristic options, providing behavioral context beyond the 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 four sentences with no wasted words. It front-loads the core purpose and efficiently provides usage context, alternatives, and a key parameter hint. Every sentence serves a clear 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 complexity (6 params, output schema exists, sibling tools), the description covers the essential purpose, usage, and alternatives. It does not explain how multiple cost fields (time, cost, risk) are used, but the heuristic enum and schema descriptions partially address this. The presence of an output schema reduces the need to describe return values.
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 67% (high), so baseline is 3. The description adds a usage tip for kPaths but does not elaborate on nodes/edges structure or other parameters beyond what schema already provides. The schema descriptions for heuristic, start, end, and kPaths are adequate.
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 finds shortest or k-shortest paths using A* + Yen's algorithm, with specific verb and resource. It distinguishes from siblings by mentioning analyze_graph and solve_schedule as alternatives for different tasks.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly lists use cases (routing, dependency resolution, critical-path discovery) and provides direct alternatives for when not to use this tool (analyze_graph for graph structure, solve_schedule for scheduling). Also gives guidance on setting kPaths>1 for 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?
Discloses stochastic initialization leading to slight differences between runs, and the requirement for ORACLAW_API_KEY. Annotations already indicate non-destructive and read-only nature (readOnlyHint=true, destructiveHint=false), so description adds useful behavioral context without 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?
Description is brief and front-loaded with key information (premium, continuous optimization, CMA-ES). Every sentence adds value, with no redundancy.
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?
Provides use cases, limitations, comparison to alternatives, stochastic behavior, and authentication requirement. Has output schema, so return values need not be explained. Complete for a complex optimization 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 100%, so baseline is 3. The description does not elaborate on parameter semantics beyond what the schema already provides, so no additional value added.
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 performs continuous black-box optimization via CMA-ES, giving specific use cases (hyperparameter search, simulator calibration). It distinguishes itself from sibling tools (optimize_evolve for discrete, solve_constraints for LP/MIP).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly tells when to use (non-convex, noisy, gradient-free continuous optimization) and when not to (discrete or LP/MIP problems), with alternative tool names provided.
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=true, destructiveHint=false, idempotentHint=true, openWorldHint=true, so the safety profile is clear. The description adds interpretive guidance ('Lower Brier/log score = better') and explains input alignment ('predictions[i]... outcomes[i]...'), which enriches behavioral understanding without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise at ~100 words, front-loads the main purpose, and every sentence adds value. It includes purpose, usage, metric interpretation, input alignment, alternative tool, and a note about being free, all without redundancy.
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 presence of an output schema (not shown but indicated), the description does not need to explain return values. It covers purpose, usage, parameter alignment, and alternatives, providing complete context for an agent to use the tool correctly.
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 parameters are already documented. The description adds context by explaining the index alignment between predictions and outcomes, which is not in the schema. This adds some value but does not provide extensive additional semantics beyond 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?
The description clearly states the tool's function: scoring calibration of probability predictions using Brier and log scores. It specifies the verb 'score' and the resource 'calibration', and distinguishes itself from the sibling 'score_convergence' by explicitly stating the alternative use case.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly indicates when to use (evaluate forecaster accuracy, model calibration, prediction-market fairness) and when not to ('for comparing multiple forecasters' agreement, use score_convergence instead'). Provides clear direction for the agent.
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=true and idempotentHint=true. The description adds that the default outcome metric is the sum of input variables and that scenarios vary individual drivers. This goes beyond annotations, though it could mention output structure.
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, front-loaded with the main action, then use cases, then additional detail and alternative. Every sentence earns its place.
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 schema coverage is 100% and output schema exists, the description covers purpose, usage, default behavior, and alternatives. It feels complete for this tool's complexity.
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 restates that scenarios override baseCase variables, which is already in the schema. It does not add significant new semantics beyond overall context.
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?
Explicitly states it compares named what-if scenarios against a base case, returning per-scenario outcome delta and sensitivity ranking. The verb 'compare' and resource 'scenarios' are specific, and it distinguishes from simulate_montecarlo.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit use cases: budget sensitivity analysis, deal what-ifs, capacity planning. Also gives a clear alternative: 'For random sampling from a distribution, use simulate_montecarlo.'
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=true, idempotentHint=true, etc. The description adds 'Deterministic' behavior, which is beyond annotations. It does not contradict annotations. It could detail edge cases but is sufficient given annotation coverage.
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 three sentences, efficient and front-loaded with the main action and key concepts. No unnecessary words or repetition.
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 moderate complexity (two array parameters with nested objects), annotations covering safety and idempotency, and an output schema (present), the description provides sufficient context: purpose, selection criteria, and behavioral note (deterministic). It does not need to explain return values due to output schema.
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%, so the description should compensate. It adds the key semantic of 'matching task energy requirements with slot energy levels' which explains the 'energyRequired' and 'energyLevel' fields. However, it doesn't elaborate on task 'priority', slot 'duration', or other properties beyond what names and schema describe.
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 (Assign tasks to time slots) and resource (schedule), and explicitly distinguishes from siblings 'solve_constraints' and 'plan_pathfind' by naming them and specifying different use cases (general resource allocation, sequence/route problems).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit when-to-use guidance ('Use specifically for task→slot assignment with energy matching') and when-not-to-use by naming alternatives for different problem types. It also lists concrete applications (deep-work scheduling, shift planning, exercise scheduling).
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 readOnlyHint, idempotentHint, and openWorldHint. The description adds that the tool learns from history to adapt preferences and returns selected arm with expected reward and confidence width, providing behavioral context beyond annotations. No contradictions found.
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 (~60 words) and well-structured: first sentence states purpose, second gives usage context, third contrasts with sibling, fourth specifies output. Every sentence serves a clear function with 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 schema coverage and existence of output schema, the description covers key aspects: functionality, when to use, alternative, and output. Minor omission like min arms requirement is in schema. Overall sufficient for an AI agent to correctly invoke the tool.
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 high (75%), so baseline is 3. The description adds value by explaining how to use the history parameter ('seed the model'), which goes beyond the schema's 'Optional past observations'. This incremental guidance justifies a 4.
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 picks the best option using a contextual bandit algorithm (LinUCB). It identifies the resource (options/arms) and the action (optimization based on context). It also distinguishes itself from the sibling tool optimize_bandit by specifying when to use each.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance: use when the best option depends on per-call context features, and if not, use optimize_bandit. It also advises passing observed history for learning, covering both when-to and when-not-to use the tool.
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 read-only, non-destructive, idempotent, open-world. The description adds context: premium feature, requires API key, and explains that it uses historical data and computes VaR/CVaR. 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?
Description is concise: a few sentences front-loaded with purpose, followed by use case, alternative, and requirement. No wasted words.
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 output schema exists, the description provides sufficient context: input format, use cases, alternative tool, and access requirements. Covers the complexity well.
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 descriptions exist for all parameters. The overall description clarifies the 'returns' parameter format as matrix and explains horizon scaling. Adds value beyond 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?
The description clearly states the tool computes VaR and CVaR from historical returns, accounting for cross-asset correlation. It lists specific use cases and distinguishes from the sibling tool simulate_montecarlo.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly tells when to use (risk attribution, regulatory capital, drawdown analysis) and when not to use (parametric simulations) by naming the alternative. Also notes premium status and API key requirement.
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 read-only and idempotent. Description adds premium requirement (ORACLAW_API_KEY) and return details (indices, values, statistics), improving transparency 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?
Five sentences efficiently convey purpose, usage, parameters, and limitations, with no redundancy.
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?
With full schema coverage, annotations, and output schema, description adds algorithm rationale, return structure, and a sibling reference, making it complete.
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 covers all parameters with descriptions. Description adds contextual defaults (thresholds) and method selection criteria (Z-score vs IQR), enhancing semantic understanding.
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 detects outliers in numeric series using Z-score or IQR, specifying use cases like monitoring metrics, fraud signals, etc. It distinguishes from sibling tool predict_forecast.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly explains when to use Z-score (near-normal data) vs IQR (heavy tails), and directs to predict_forecast for projection, providing clear decision guidance.
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 safety profile (readOnly, destructive, idempotent, openWorld). Description adds behavioral context: how prior is seeded (Beta(prior*10, (1-prior)*10)), that it produces a calibration score, and is free. 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?
Four sentences: purpose, explanation, use cases, alternatives. No unnecessary words, front-loaded with action verb, 'Free.' appended. Every sentence earns its place.
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 moderate complexity, presence of output schema (not needing return value doc), and complete annotations, the description fully covers purpose, parameters, usage context, and alternatives. No gaps identified.
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 has 100% description coverage, so baseline 3. Description adds the Beta prior seeding formula and clarifies evidence format (value in [0,1], weight). This provides meaning beyond schema definitions.
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 the tool updates a prior probability with weighted evidence using Beta-Bayesian posterior, specifies exact use cases (fraud-risk scoring, A/B test stopping, diagnostic probability stacking), and distinguishes from siblings predict_ensemble and simulate_montecarlo.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use (incremental belief revision) and when not to: for combining independent model predictions use predict_ensemble, for full distribution sampling use simulate_montecarlo.
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 declare read-only and idempotent, but description adds premium tier, API key requirement, and confidence bands. 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?
Single efficient paragraph, front-loaded purpose, no fluff, every sentence adds value (use case, model choice, requirements, alternative).
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?
Covers all key aspects: purpose, model selection, data needs, API key, premium tier, and alternative tool, despite presence of output schema (not shown).
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 has 100% description coverage, so baseline 3. Description adds method selection guidance and data constraints (e.g., Holt-Winters needs 2× seasonLength), beyond 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 clearly states projecting future values from univariate time series using ARIMA or Holt-Winters, specifies use cases (demand planning, KPI projection), and distinguishes sibling detect_anomaly.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly tells when to use ARIMA vs Holt-Winters based on data seasonality, mentions observation requirements, and directs to detect_anomaly for anomaly detection instead.
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 behavioral context beyond annotations: it discloses the output ('returns an aggregate convergence score, the consensus probability, and per-pair disagreement'), mentions 'free tier', and implies reading behavior consistent with annotation readOnlyHint=true. 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 concise (two sentences plus a brief note), front-loaded with the core purpose and usage, and every sentence adds value without redundancy.
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 complexity (nested objects, many optional fields, output schema present), the description is complete: it explains purpose, usage, output, and alternative. With an output schema, return values need not be described in detail.
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 100%, so the baseline is 3. The description does not add new parameter-level details beyond what the schema already provides, but it also does not need to since the schema is comprehensive.
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 action ('score how much multiple independent sources agree'), identifies the resource ('probability estimate'), and distinguishes it from siblings by mentioning 'for comparing pre-binned distributions, prefer this over simulate_montecarlo'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly recommends use cases ('fuse signals from polling, prediction markets, model ensembles, or any source emitting a 0..1 probability') and provides an alternative ('for comparing pre-binned distributions, prefer this over simulate_montecarlo'), giving clear when-to-use and when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true (safe read). Description adds return info (arm + score breakdown) and performance guarantee (<1ms). No contradictions. Adds value beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with core purpose, then usage guidance, then return/performance. Every sentence adds value, 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?
Comprehensive for a bandit selection tool: purpose, use cases, alternatives, parameter semantics, output description, performance. No gaps given existing schema and annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Adds meaning beyond 100% schema coverage: clarifies that arms need reward history (pulls, totalReward) and explains algorithmic behavior (UCB1 deterministic, thompson/epsilon-greedy sample).
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?
Clearly states 'Pick the best option from a set of variants' and specifies algorithms (UCB1, Thompson, ε-greedy). Distinguishes from siblings optimize_contextual and optimize_cmaes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly tells when to use (options with reward history, explore/exploit tradeoff) and when not to use (context-dependent → optimize_contextual, continuous tuning → optimize_cmaes).
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 provide readOnlyHint, destructiveHint, idempotentHint, openWorldHint. Description adds specifics: each call re-samples (non-idempotent), capped at 2000 iterations free. 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?
Single concise paragraph, front-loaded with core functionality, then usage, alternative, behavioral notes. Every sentence adds value.
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?
Covers all essentials: 6 distributions, parameter requirements, non-idempotence, free tier cap. Output schema exists for return values, so no need to detail them.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with detailed descriptions. Description adds meaning by noting required keys depend on distribution and specifying defaults (1000) and limits (2000 free).
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 starts with a specific verb+resource ('Sample N draws from a parametric distribution') and clearly lists returned outputs. It provides concrete use cases and distinguishes from sibling tool analyze_risk.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use (quantify uncertainty around a single random factor) and when not to (multi-asset portfolio, use analyze_risk). Mentions behavioral details like non-idempotent and free tier cap.
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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