ModelCostSaver

by sachinuppal

Overview Schema Related Servers Score Discussions

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Server Quality Checklist

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Latest release: v0.1.1
Disambiguation2/5
Several tools have overlapping purposes (e.g., compare_models, predict_cost, optimize_request, select_optimal_model all compare or select models based on cost). Additionally, get_pricing and list_models have identical descriptions, causing ambiguity.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern using snake_case (e.g., estimate_cost, list_models, select_optimal_model).
Tool Count5/5
7 tools is well-scoped for a model cost estimation server, covering the essential operations without being excessive.
Completeness4/5
The tools cover listing, pricing, cost estimation, comparison, and optimization. However, the presence of a duplicate tool (get_pricing and list_models) indicates a minor gap in tool design.
Average 3.4/5 across 7 of 7 tools scored. Lowest: 2.8/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 3 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It mentions 'Offline' which suggests cached data but is ambiguous. It does not explicitly state it is read-only or disclose any 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short but includes an ambiguous phrase 'capabilities are arrays' and 'Offline'. Every sentence could be more informative, but it is not overly verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 5 parameters and an output schema, the description is minimal. It does not explain the 'Offline' aspect or how caching works, nor does it provide context for the pricing data.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% description coverage for parameters, so baseline is 3. The description adds minimal meaning beyond 'capabilities are arrays', which is somewhat unclear and does not enhance parameter understanding.
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 returns the model catalog with pricing and supports optional filtering. However, it does not explicitly distinguish itself from the sibling tool 'list_models', which could be a potential overlap.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'estimate_cost' or 'compare_models'. The description lacks context for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description only says it picks cheapest model with reasoning and fallbackChain; no details on side effects, auth requirements, or how 'offline' affects behavior.
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?
Description is very concise (one sentence) with no waste, though it could benefit from slight expansion.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema, the description is too brief for a complex selection tool; missing explanation of 'task tier', 'fallbackChain', and how inputs are used.
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 high (89%), so baseline 3. Description adds only minor context (e.g., mapping task tier to parameters), not enough to raise score.
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 selects the single cheapest model meeting task tier, capabilities, and budget, distinguishing it from siblings like compare_models or estimate_cost.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives; no when-not-to-use or when-to-use-this particular context provided.
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?
With no annotations, the description bears full burden for behavioral disclosure. It notes that capabilities are arrays and that data is offline (static), which adds value. However, it omits details on data freshness, pagination, rate limits, or any side effects. The 'Offline' hint is useful but insufficient for full transparency.
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 very concise, with only two short sentences and a fragment. It front-loads the main purpose. However, the fragments ('capabilities are arrays. Offline.') are not grammatically integrated, which slightly detracts from structure. Overall efficient but could be more polished.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (list with optional filters) and the presence of an output schema, the description is adequate but minimal. It does not mention pagination, sorting, or any limits, which would be useful for large catalogs. It meets basic needs but leaves gaps for complex queries.
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 adds minimal parameter context beyond the schema: 'optionally filtered' and 'capabilities are arrays' hint at filtering but do not specify syntax. It does not significantly enhance the schema's descriptions, so the score remains at baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states it returns the model catalog with pricing and optional filtering, which clearly communicates the tool's purpose. The name and title align well. However, it does not explicitly differentiate from sibling tools like get_pricing or compare_models, but the purpose is still clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as compare_models, select_optimal_model, or get_pricing. It lacks any 'when to use' or 'when not to use' context, making it hard for an AI to choose appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description should disclose behaviors. It states 'Offline' but does not clarify what that entails (e.g., no network call, local cache). No mention of permissions, side effects, or data sources. The sorting and ratio calculation are mentioned 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
One sentence covering key aspects: comparison, sorting, ratio, offline. It is efficient but could be more structured (e.g., bullet points). No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With output schema present, return values are covered. However, ambiguity about 'Offline' and lack of explanation for 'multiple of the cheapest' leaves gaps. Adequate for a simple comparison tool but not fully comprehensive.
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 all parameters are documented. The description adds limited value beyond schema: 'fixed token shape' reinforces input/output tokens, but no extra formatting or constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool compares models side by side for a fixed token shape, sorts cheapest first, and includes the multiple of the cheapest. It mentions 'offline' and distinguishes from sibling tools like estimate_cost and get_pricing by focusing on comparison.
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?
No explicit guidance on when to use this tool vs siblings such as select_optimal_model or estimate_cost. The description implies usage for fixed token shapes and offline computation, but does not specify prerequisites or exclude alternatives.
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?
With no annotations, the description carries full burden. It discloses offline behavior and no key requirement, but lacks details on idempotency, caching, error conditions, or whether it uses live pricing data. Adds some transparency but insufficient for a tool with zero 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 concise sentences, front-loaded with purpose. No fluff or redundancy. Every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 6 params and an output schema available, the description covers basic purpose and key behavioral trait (offline, no keys). However, it doesn't differentiate from similar sibling predict_cost, nor explain expected input format details or error cases. Adequate but not thorough.
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 the description need not repeat parameter details. It adds the global context 'Offline, no keys' and mentions charsPerToken override, but no additional semantics beyond the schema for individual parameters. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states verb 'estimate cost' and resource 'single LLM call for one model'. Distinguishes from siblings like compare_models, get_pricing, and predict_cost by focusing on a single call with known/estimated token counts and emphasizing offline usage.
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?
Mentions 'Offline, no keys' implying no API call needed, but does not explicitly state when to use this tool versus alternatives like predict_cost or select_optimal_model. No 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.
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 states 'Offline' (no actual call) and reports savings, but does not disclose read-only nature, permissions, rate limits, or other side effects. More detail would be helpful.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, highly concise, and front-loads the purpose. Every word earns its place with no waste.
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 has 8 parameters and an output schema, the description provides the essential purpose and offline nature. With output schema, return details are not needed. A bit more context on parameter roles (e.g., taskClass) would improve completeness, but it is mostly adequate.
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 88% (high), so baseline is 3. The description does not add meaning beyond the schema for parameters. It sets context but does not elaborate on how parameters like taskClass or crossProvider affect the recommendation.
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 'Check' and resource 'cheaper capable model for a call you plan to make,' and reports savings. It is specific and distinguishes from sibling tools like compare_models or select_optimal_model by focusing on a planned call and being offline.
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 mentions 'Offline' implying use before making a call, but it does not explicitly state when to use vs. alternatives like compare_models or select_optimal_model. No when-not or alternative guidance is provided.
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?
With no annotations, the description carries the burden. It states 'Offline' and 'returns a cheapest-first ranking with assumptions', which hints at read-only and non-destructive behavior. However, it does not detail error handling, rate limits, or implications of 'assumptions'.
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 concise sentences that front-load the action and outcome. Every word 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 presence of an output schema and 10 parameters, the description is minimally complete but could better explain its relationship to sibling tools. It covers the core purpose and behavior sufficiently for an agent to select 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 coverage is high (80%), so baseline is 3. The description adds no extra meaning beyond the schema; it does not explain the role of key parameters like 'providers' or 'target'.
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 ('forecast'), the resource ('cost of a prompt across candidate models'), and the output ('cheapest-first ranking'). It distinguishes itself from siblings like 'estimate_cost' and 'compare_models' by emphasizing it is done 'before the call' and is 'offline'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use before making a call but does not provide explicit guidance on when to use this tool versus alternatives such as 'estimate_cost' or 'select_optimal_model'. No exclusions or scenarios are mentioned.
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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