mcp-chainladder

by datalattice

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Server Quality Checklist

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Latest release: v1.2.3
Disambiguation5/5
Each tool has a clearly distinct purpose: core calculation (compute_chain_ladder), alternative method (bornhuetter_ferguson), comparison, diagnostics (raw and interpreted), stochastic error, sensitivity, tail extrapolation, data I/O, conversions, license status. No overlap.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with snake_case, e.g., compute_chain_ladder, mack_diagnostics, parse_csv_triangle. The style is uniform and predictable across all 14 tools.
Tool Count5/5
14 tools is well-scoped for an actuarial reserving server, covering core methods, diagnostics, error estimation, sensitivity, tail extrapolation, data I/O, and conversions. No extraneous tools, and each serves a necessary function.
Completeness5/5
The tool set covers the full typical reserving workflow: data input (parse_csv_triangle, sample_triangle), core deterministic calculation, alternative method, comparison, diagnostics, stochastic error, sensitivity analysis, tail extrapolation, and format conversions. No obvious gaps.
Average 4.4/5 across 14 of 14 tools scored. Lowest: 3.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No stable releases found
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- No high-severity vulnerability alerts
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This repository is licensed under MIT License.
This repository includes a README.md file.
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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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses the iterative process, ranking, and return types (including license failure). However, it doesn't mention side effects, performance implications, or the 'Pro tier' licensing details beyond a hint.
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 well-structured with separate sections for behavior, usage, args, and returns. It is concise yet informative, with no unnecessary fluff. Every sentence adds value.
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 actuarial complexity and presence of output schema, the description covers purpose, usage, parameters, and return values (including error case). It is fairly complete, though could add more on prerequisites or limitations.
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 0%, but the description includes an Args section explaining each parameter with context (e.g., triangle 'as in compute_chain_ladder', excluded 'honours existing exclusions'). This adds significant value beyond the bare schema.
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's action: dropping each link ratio, rerunning chain ladder, and ranking impact on IBNR. It provides specific verb and resource, with context about 'Pro tier' and use after mack_diagnostics, though it doesn't explicitly differentiate 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 Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit guidance: 'Use after mack_diagnostics flags outliers — these are the cells to investigate first.' This gives clear when-to-use context, but lacks explicit when-not-to-use or alternatives beyond the implied order.
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, the description carries full burden. It discloses that results include total deltas and the single AY with largest divergence, and mentions possible license failure. It does not detail error handling for invalid inputs, but overall provides good behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with purpose and usage guidance, followed by parameter list and return values. It is well-structured but slightly lengthy with the detailed return fields. Good balance of completeness and conciseness.
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 5 parameters with 2 required and an output schema present, the description covers purpose, usage, parameter references, and return fields including error case. Reliance on cross-reference for parameter details is a minor gap, but overall contextually complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so description must compensate. The description references 'As in compute_chain_ladder' for triangle, selected_factors, tail, excluded, and explains a_priori_ultimates. This adds meaning but relies on cross-reference, which is minimal 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 runs chain ladder and Bornhuetter-Ferguson on the same triangle and returns a side-by-side comparison. It distinguishes itself from sibling tools that compute individual methods.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: 'The canonical second opinion — when both methods agree, the reserve is defensible; when they diverge, the divergence is the finding.' It also notes the Pro tier, implying licensing requirements. However, it does not explicitly state when not to use this tool or suggest 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, but the description discloses that the tool returns either a success object with detailed test results or an error object for license failure. It does not mention side effects or permissions, but the 'Pro tier' designation and license error imply access control. The description is thorough but stops short of stating whether the tool is read-only.
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 well-structured with a clear flow: core purpose, feature list, usage cues, parameter list, return value structure. It is concise for the amount of information conveyed, though the feature list could be slightly compressed.
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 no annotations and an output schema that presumably captures return structure, the description covers the essential aspects: what the tool does, when to use it, return value on success and failure, and high-level parameter meanings. It lacks detailed parameter formats but compensates with example usage scenarios.
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 must add meaning. It lists the four parameters but provides minimal detail: 'triangle: As in compute_chain_ladder' is vague, and 'excluded: Outlier exclusions to honour' lacks specificity. The defaults are mentioned only for outlier_threshold. Given the lack of schema descriptions, the parameter explanations are insufficient to fully understand input formats.
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 executes Mack assumption diagnostics and labels results with verdicts and recommendations. It distinguishes itself from the free-tier 'mack_diagnostics' by listing four specific added capabilities (verdict band, summary, recommendation, overall verdict).
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 usage cues: 'Use this when the user asks "is the chain ladder OK?", "should I publish this?", or "what does the model tell me about itself?".' Also contrasts with free-tier 'mack_diagnostics' to guide when to use this Pro version.
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?
Without annotations, the description carries full burden. It discloses key behaviors: fills only unobserved cells, nan for rows with no observation, returns a dict with disclaimer. However, it does not mention error handling (e.g., factor length mismatch) or side effects, but overall is transparent for a projection operation.
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 (a few sentences), uses clear section headers (Args, Returns), and front-loads the main purpose. Every sentence provides necessary information without repetition or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (projection using chain-ladder), the description covers inputs, constraints, output format, and a notable behavioral detail (nan for no-observation rows). It references an auxiliary tool for triangle format, which is acceptable. Minor gap: no explanation of the 'disclaimer' purpose.
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?
With 0% schema coverage, the description adds significant meaning: triangle is 'as in compute_chain_ladder' (context), selected_factors has a length constraint and description of 'one factor per development-period transition'. This goes beyond the schema's mere names and types, though the triangle format could be more explicit.
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: 'Fill the lower-right (unobserved) cells of a cumulative triangle using the supplied age-to-age factors.' It uses a specific verb ('Fill') and resource ('cells of a cumulative triangle'), and distinguishes itself from sibling tools like compute_chain_ladder by focusing on projection with precomputed factors.
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 age-to-age factors are already supplied (e.g., from compute_chain_ladder) but does not explicitly state when to use this tool versus alternatives. It lacks 'when-to-use' or 'when-not-to-use' guidance, leaving the agent to infer context from the sibling set.
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 full burden. It discloses that unobserved cells propagate as null and the relationship to to_incremental, but does not cover edge cases, permissions, or side effects. The description is adequate but minimal.
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 short, front-loaded with purpose, and uses a clear Args/Returns structure. Every sentence serves a purpose 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 the simple tool (1 parameter, transformation, output schema present), the description covers input, behavior, null propagation, inverse relationship, and output format. The 'standard disclaimer' reference is vague but acceptable as a convention. Nearly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter 'incremental' is described as 'Incremental triangle' with 0% schema description coverage. While this adds semantic context beyond the raw schema (which only gives type), it lacks detail on shape, constraints, or examples. The description adds some value but is basic.
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 converts an incremental triangle to cumulative via running sum per row. It differentiates itself from the sibling to_incremental by explicitly naming it as the inverse operation.
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 it is the inverse of to_incremental, providing clear guidance on when to use this tool versus its sibling. This is strong usage 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?
No annotations are provided, so the description carries full burden. It discloses that unobserved cells stay unobserved, provides the exact formula, and mentions the return value structure (dict with 'triangle' and disclaimer). This is good transparency for a transformation 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, using formulas and clear statements. It is front-loaded with the purpose, then provides the formula and a note about unobserved cells. No wasted sentences.
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 existence of an output schema, the description sufficiently explains the return value. The tool is simple, and all necessary information (input format, transformation logic, treatment of nulls) is covered.
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?
With 0% schema description coverage, the description adds meaning by describing the parameter as 'Cumulative triangle, possibly with unobserved cells.' This goes beyond the schema's type definition and helps the agent understand the expected input.
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 'Convert a cumulative triangle to incremental (per-period) values', providing a specific verb and resource. The sibling tool 'to_cumulative' does the opposite, so it distinguishes itself effectively.
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 explains the mathematical transformation but does not explicitly state when to use this tool vs. alternatives like 'to_cumulative'. Implicitly, it's for converting cumulative to incremental, but no when-not-to or contextual guidance is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden and discloses specific behaviors: handling of empty cells and tokens, stripping commas, dropping header rows, padding jagged rows, and path requirements. It lacks error handling or performance details but is still informative.
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 well-structured with separate sections (Args, Returns) and front-loaded with the main purpose. It is somewhat lengthy but each sentence adds value; 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?
Given the tool's simplicity (1 parameter, no nested objects) and presence of an output schema (implied by 'Has output schema: true'), the description fully covers the tool's behavior, return values, and use case, leaving no significant gaps.
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?
The single parameter 'path' has no description in the input schema (coverage 0%). The tool description adds critical details: path must be absolute or ~-relative and readable by the server process, providing meaning beyond the schema type.
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 with a specific verb ('Parse'), resource ('CSV file'), and outcome ('cumulative loss triangle'). It distinguishes itself from sibling tools that perform different operations (e.g., compute_chain_ladder, project_triangle).
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 explicitly provides a usage condition: 'Use this when the user gives you a CSV file path and wants to run the chain ladder on it.' While it does not list exclusions, the guidance is clear and relevant.
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 the three statistical tests performed, outlier detection method, and return structure including formula for residuals. No side effects mentioned, but appropriate for a read-only diagnostic tool.
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?
Well-organized with Args, Returns, and Verdict guidance sections. Front-loaded with purpose. Slightly lengthy but justified by the complexity of the tool.
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 complete information including return values with types and interpretation guidance. Given the presence of an output schema (described in text), the description covers all necessary context for correct invocation.
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 detailed descriptions for all 4 parameters beyond the schema (which has 0% coverage). Explains each parameter's role, including defaults and relationships (e.g., selected_factors length, outlier_threshold default).
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 performs Mack (1994) assumption diagnostics including three tests and outlier detection, and gives example use cases. Distinguishes itself from siblings like compute_chain_ladder and interpret_diagnostics by focusing on statistical tests.
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 clear usage scenarios: when user asks about chain ladder assumptions, outliers, or specific effects. Implicitly ties to compute_chain_ladder but lacks explicit when-not-to-use or alternative tool names.
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?
The description discloses the return structure (triangle with lower-right null, n_acc=10, n_dev=10, expected_totals) and mentions it's for parity checking. Since no annotations exist, it carries full burden and adequately describes behavior without 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 concise with a front-loaded main purpose, followed by structured return fields. 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?
The description fully covers the tool's purpose, output structure, and context (sample data for demos). With no parameters and an output schema available, it provides sufficient information for correct use.
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?
There are no parameters, so baseline is 4. The description doesn't need to add parameter meaning, and it doesn't attempt to.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns a 'classic textbook 10×10 cumulative-paid triangle (Friedland-style)' and mentions use cases like demos, examples, and server verification. It distinguishes from sibling tools by being a sample dataset, while siblings like parse_csv_triangle handle real data.
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 explicitly states when to use the tool (demos, examples, verifying server is working) but doesn't explicitly mention when not to use it or suggest alternatives. However, the context of being a sample triangle implies it's not for real analysis.
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, the description carries full burden. It explains the fitting process (two models, R² selection), return types (success or license error), and mentions the 'Pro tier' license. It is transparent about the computation but could add more detail on side effects (none) or prerequisites beyond license.
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 well-structured with a header, explanation, usage guidance, parameter descriptions, and return format. It is concise with no wasted words, front-loaded with the core action.
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?
Despite the tool's complexity (model fitting, multiple return paths, license dependency), the description covers all essential aspects: what it does, when to use, parameters, return structure, and error handling. The presence of an output schema further completeness.
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?
The input schema has 0% description coverage, but the description provides clear explanations for both parameters: selected_factors as 'the factor set to extrapolate from' and n_extra with default 6 and context 'covers most P&C lines'. This adds significant meaning beyond the schema types.
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 fits parametric tail models to late development factors and extrapolates forward, specifically for chain ladder tail factor. It distinguishes itself by mentioning plugging into compute_chain_ladder, a sibling tool, and specifying the two models used. The purpose is unambiguous and specific.
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 explicitly states when to use the tool: when the triangle has not reached ultimate and the last factor is above 1. It also links to compute_chain_ladder for integration. However, it does not explicitly mention when not to use it or list alternative tools, though no direct alternative exists among siblings.
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 discloses behavior: it performs the BF method, returns both BF and chain-ladder results, and handles license failures by returning an error with upgrade URL. No contradictory or missing details.
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 well-structured with a summary, args, and returns sections. It is slightly verbose but every sentence adds value; could be tightened slightly.
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 complexity (5 parameters, output schema, license tier), the description is thorough: it explains input formats, defaults, return fields, and error behavior. No gaps.
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 description coverage is 0%, so the description carries full burden. It provides detailed explanations for all 5 parameters, including defaults (tail=1.0, selected_factors null) and relationships, adding significant value 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 identifies it as the Bornhuetter-Ferguson reserving method, explains its purpose (combines chain-ladder development with a priori ultimate), and distinguishes it from siblings like compute_chain_ladder by positioning it as a benchmarking method.
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 it (canonical second method for benchmarking reserves) and how it relates to chain ladder, but does not explicitly state when not to use it or mention alternative tools by name.
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 fully bears the responsibility of disclosing behavior. It thoroughly explains the computation, parameters, and output structure, including handling of nulls and the effect of optional arguments. This provides complete transparency for a mathematical tool.
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 well-structured with a brief intro followed by clearly labeled Args and Returns sections. While it is detailed, every sentence provides necessary information. It could be slightly more concise, but the organization makes it easy to parse.
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 complexity of chain-ladder reserving and the presence of an output schema, the description is remarkably complete. It covers all inputs, default behaviors, and each field of the return dictionary. No gaps are evident for typical usage scenarios.
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?
The input schema has 0% description coverage (no parameter descriptions in the schema). The tool's description compensates fully with detailed explanations of each parameter (triangle format, selected_factors defaults, tail default, excluded format). This adds significant value beyond the schema's types and titles.
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 'Run a full chain-ladder reserving calculation on a cumulative loss triangle' and gives explicit usage cues like 'call it whenever the user asks "what's the IBNR?".' This distinguishes the tool from siblings such as bornhuetter_ferguson and mack_diagnostics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides context for when to use the tool ('when the user asks... or hands you a triangle and asks for projections') and labels it as the 'workhorse tool.' However, it does not explicitly mention when not to use it or contrast it with alternatives, which would improve the score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 describes the output in detail, mentions backfilling via Mack's tail rule and cross-row covariance, but does not explicitly state side effects or authorization needs. Given the computational nature, transparency is strong though not exhaustive.
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?
Well-structured with a clear purpose paragraph, usage paragraph, and structured Args/Returns sections. Every sentence adds value, concise yet comprehensive.
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 3 parameters, explains all 5 return fields with types and interpretations, and references sibling tool for consistency. Given the output schema exists, the description is complete for an experienced user.
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 description coverage is 0%, but the description adds comprehensive parameter details: triangle references compute_chain_ladder, selected_factors includes length constraint, and excluded explains shape and nullability. This fully compensates for the schema 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 the tool performs stochastic chain-ladder error estimation, returning standard errors and coefficients of variation. It distinguishes from sibling tools like compute_chain_ladder (point estimate) and mack_diagnostics by focusing on uncertainty quantification.
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 advises use when the user asks about 'uncertainty, reserve risk, or confidence intervals' and recommends pairing with compute_chain_ladder using the same selected_factors for consistency. Provides clear context for when to invoke.
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 provided, the description fully covers behavior: it states it is non-destructive, free to call, and explains each returned field in detail, including the role of the 'message' for Claude to relay.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: one sentence for purpose, then a bulleted list for returns. Every sentence adds value, 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?
The tool is simple (0 params), and the description fully explains what it does, when to call it, and what the output contains. No gaps remain given the low complexity and presence of output schema (documented in description).
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?
There are zero parameters, so the description does not need to add meaning beyond the schema. The baseline for 0 params is 4, but the description excels by also detailing the output semantics, which is a bonus.
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 inspects the Pro-tier license state and lists the exact return fields. It is distinct from all sibling tools, which are focused on insurance reserving methods, leaving no ambiguity.
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 indicates the tool is 'free to call regardless of license state,' which advises that it is safe to use anytime. No explicit when-not-to-use is needed due to the tool's simple nature and distinct purpose from siblings.
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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