mcp-sputnikx-market

Server Quality Checklist

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Latest release: v2.3.0
Disambiguation3/5
Most tools have distinct purposes, but there are overlapping areas: search_products, get_prices, and check_availability all deal with product information; query_trade and query_customs both handle trade data; salary_lv_wages and salary_overview both involve salary data. Descriptions help, but the boundaries are not always clear.
Naming Consistency2/5
Naming is inconsistent: some tools use verb_noun patterns (search_products, get_prices, check_availability), while others are noun phrases (soul_profile, salary_lv_wages), and 'calculator' is a lone noun. This mix of styles makes it harder to predict tool names.
Tool Count3/5
With 19 tools, the server straddles the upper boundary of acceptable scope. It's not overwhelmingly large, but the count feels heavy given that the tools span several unrelated domains, making it harder to navigate.
Completeness2/5
The tool surface is incomplete for a cohesive domain. Each cluster has gaps: products lack order cancellation or update; soul tools lack actions like claiming bounties; skills only have list/run, no creation or management. The overall domain is ambiguous, so it's unclear what complete coverage would look like.
Average 3.6/5 across 19 of 19 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
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- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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This repository is licensed under MIT License.
This repository includes a README.md file.
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}
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does not state whether this is a read-only operation, what the response structure looks like, or any limitations (e.g., data availability, aggregation). It only lists data dimensions, leaving the agent to infer 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?
The description is a single terse phrase that is front-loaded with the topic and key breakdowns. It wastes no words, though it is more of a label than a full sentence.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description should explain what data is returned (e.g., wage values, units, time span) and usage context. It lists breakdown dimensions but omits return format, filtering behavior, and any prerequisites, making it insufficient for reliable tool invocation.
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 input schema already describes both params (year with default, sector as NACE filter) with 100% coverage. The description's mention of 'sector' aligns with the schema but adds no new meaning; it does not explain how 'year' or 'sector' map to the output breakdowns.
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 this tool provides detailed wage data for Latvia, enumerating breakdowns (sector, occupation, gender gap, regional). However, it lacks an explicit verb and does not directly contrast with siblings like salary_overview, so it's clear but not fully differentiated.
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?
There is no guidance on when to use this tool versus salary_overview or salary_ai_risk. The description implies use for detailed Latvia-specific wage breakdowns, but no exclusions or alternatives are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It implies a read-only operation by mentioning 'metadata', but does not state whether the tool returns a summary, a list, or any other format. No information is given about side effects, rate limits, or authentication requirements. The description is too sparse to be transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise phrase, front-loaded with the key term 'EU salary database metadata'. It wastes no words and is easily scannable. However, it might be too terse to fully convey the tool's function, slightly reducing the score below perfect conciseness.
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 low complexity (no parameters, no output schema), the description provides a minimal but somewhat adequate overview. It mentions the general domains covered. However, it lacks specifics about what kind of metadata is returned (e.g., counts, names, dates) and does not outline the output structure, making it incomplete for an agent that must understand the tool's full behavior.
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 tool has zero parameters, so the baseline for this dimension is 4 regardless of description content. Since there are no parameters to explain, the description need not add parameter-level detail. The minimal note about coverage indirectly suggests what the metadata contains, which is adequate in this case.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'EU salary database metadata — countries, sectors, occupations coverage' gives a general sense of the resource (EU salary database) and its scope (countries, sectors, occupations), but lacks a clear verb indicating action. It distinguishes partially from siblings like 'salary_lv_wages' by focusing on metadata rather than actual wage data, but the phrasing is more nominal than descriptive of a tool operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance is provided on when to use this tool versus alternatives. It does not mention any specific use case, prerequisites, or contrast with sibling tools. The user is left to infer that the tool is for obtaining metadata, but no clear conditions or exclusions are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of disclosing behavior. It lists output content but does not state whether the operation is read-only, what side effects occur, or any access requirements. It also gives no hint about error behavior or response format, so the agent gets minimal insight into what invoking the tool actually entails.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with an em-dash list, making it easy to scan. It wastes no words, though the cryptic 'SX# passport' term could be clearer. Overall, it is appropriately sized for a simple one-parameter tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has low complexity (one required parameter, no output schema). The description provides a list of profile attributes but does not fully specify the return structure, the meaning of 'SX# passport', or any edge cases. It is minimally sufficient but leaves room for ambiguity.
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% because the only parameter (agent_id) is described. The description adds no extra parameter semantics beyond the schema's identifier example, so baseline score 3 is appropriate.
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 identifies the resource as an 'Agent identity profile' and lists specific content (trust score, behavioral DNA, character traits, SX# passport), which helps distinguish it from siblings like soul_verify or soul_leaderboard. However, it lacks an explicit verb (e.g., 'get' or 'retrieve'), so it falls short of the highest clarity standard.
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. Siblings like soul_verify and soul_leaderboard exist, but the description does not mention any conditions, prerequisites, or exclusions, leaving the agent to infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full responsibility for disclosing behavior. It mentions 'paper trading' which implies a simulated environment, but it does not explicitly confirm read-only operation, data freshness, or limitations. The lack of an action verb also makes it unclear whether this modifies state or simply retrieves data.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with clear front-loading of the core concept. It contains no filler words and efficiently conveys the essence of the tool.
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 existence of three different views (stats, trades, prediction) and no output schema, the description gives a high-level overview but doesn't clarify what each view returns or how the signal feed relates to the views. This is adequate for a simple read tool but not exhaustive.
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 tool description adds no additional parameter explanation; it doesn't mention 'view' or 'limit' at all. The schema's own descriptions are minimal, but the enum values and limit default provide some context.
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 indicates the tool provides prediction market signals, listing specific components like paper trading performance, active trades, and signal feed. It distinguishes itself from sibling tools by focusing on prediction market analytics. However, it lacks an explicit verb (e.g., fetch, list), making the action slightly implicit.
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. The description does not state use cases, prerequisites, or exclusions. The schema hints at different views, but the description doesn't explain when to choose this over other trading-related siblings.
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?
There are no annotations, so the description carries the full burden of disclosing behavioral traits. It discloses that results are ranked by trust score and include DNA summaries, but does not mention whether the operation is read-only, auth requirements, rate limits, or response format, leaving significant ambiguity for a data-retrieval tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, compact phrase that conveys the core purpose without redundancy. It front-loads the key concept and uses the em dash to efficiently add detail, earning a top score for conciseness.
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 absence of an output schema and annotations, the description provides only high-level information about the result. It doesn't explain what a 'DNA summary' contains, how the leaderboard is structured, or any edge cases, leaving the agent with questions about the response format, but the tool itself is simple with one optional parameter.
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 single parameter 'limit' is fully documented in the input schema with its description and default. The tool description adds no additional meaning about parameter formatting, constraints, or usage, so it relies entirely on the schema, which is acceptable at the baseline of 3.
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 identifies the tool as an agent trust leaderboard, specifying the ranking criterion (trust score) and included content (DNA summaries). It lacks an explicit verb like 'list' or 'returns', so it's a noun phrase rather than an imperative, but the purpose is still clear and distinct from sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus its siblings, such as soul_verify or soul_profile. The description doesn't mention alternatives, exclusions, or specific contexts, leaving the agent to infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full transparency burden. It adds some context by mentioning the 3.8GB database size, but it does not disclose output shape, pagination, performance implications, or any side effects/safety notes. For a query tool this is a notable gap since the agent has no other source of behavioral expectations.
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 concise, front-loaded sentences: a clear purpose statement followed by a compact list of supported query types. It contains no filler, no repetition of schema details, and every sentence earns its place.
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?
The tool is complex with 9 parameters and 6 query modes, yet the description does not explain parameter-to-mode combinations or indicate output/return behavior. Since no output schema exists, the description should have filled this gap but leaves the agent to infer how to correctly invoke each mode.
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 input schema already provides 100% descriptive coverage of all 9 parameters, so the description does not need to repeat them. The description's only parameter-related addition is replaying the query_type enum values, which adds no new semantic value beyond what the schema already states.
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 opens with 'Query Latvia customs analytics', using a specific verb and resource, and it enumerates supported query types (classifications, trends, top_commodities, etc.). This strongly clarifies the tool's scope and differentiates it from the sibling query_trade by focusing on Latvia customs and KN8 codes, though it does not explicitly compare against alternatives.
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 through the list of supported query types ('Supports: classifications, trends...'), which tells the agent what tasks it can fulfill. However, there is no explicit guidance on when to use this tool versus siblings like query_trade, nor any exclusions or prerequisite context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. 'Query' implies a read-only operation, and the dataset size is noted, but there is no disclosure of return format, pagination, safety profile, or behavior differences between query types. This is insufficient for a tool with 9 distinct operations.
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 with no filler. The first sentence states the action and dataset, the second lists supported query types. Every word contributes to understanding the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite 9 query types and 8 parameters, the description only lists the query types without explaining each one's intended use or output. No output schema exists, so an agent cannot predict what 'balance' or 'heatmap' returns. This is a significant completeness gap for a complex 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% with descriptions for all parameters, so the baseline is 3. The description adds minor context about HS2-CN8 codes and the 27 EU countries, but does not substantially enhance parameter understanding beyond the 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 it queries EU trade data from Eurostat COMEXT, names the dataset, and enumerates the supported query types. The scope (27 EU countries, HS2-CN8 codes) makes it specific, but it does not explicitly distinguish itself from sibling tools like query_customs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for EU trade data queries but provides no explicit when-to-use or when-not-to-use guidance. It does not mention alternatives or exclusions, leaving selection to the agent's inference from the context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits such as whether the tool is read-only, what it returns, or any side effects. It only says 'verify,' which implies a read operation, but does not specify the output format or behavior on tamper detection. No contradictions with annotations exist, but the description is too sparse 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is concise and front-loaded with the main verb and object. It avoids unnecessary words and efficiently conveys the tool's core function and purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, so the description should explain what the tool returns or what 'verify' entails. It does not clarify the output, the verification process, or any additional context about the hash chain. For a simple but potentially compliance-critical tool, more information is needed.
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 input schema provides a description for the single parameter 'agent_id' ('Agent to verify'), so schema coverage is high. The tool description adds no additional meaning beyond the schema, so the baseline score of 3 applies.
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 with a specific verb ('Verify') and a well-defined resource ('agent event hash chain integrity'). It also provides the purpose ('tamper detection for EU AI Act compliance'), which distinguishes it from siblings like soul_profile or soul_leaderboard that serve different functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for tamper detection and compliance verification, but does not explicitly state when to use this tool versus alternatives or mention any exclusions. The context of EU AI Act compliance suggests a use case, but no direct 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 discloses auth requirements and latency but does not state whether the operation is read-only or has side effects. It could be more transparent about the impact and return format.
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, each providing necessary information: purpose, behavior, auth, and latency. It is concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the core purpose, prerequisites, and latency. With no output schema, it could explain the analysis structure, but the format parameter partially addresses this. Overall, it's fairly 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 100%, so the schema already documents all parameters. The description does not add meaningful parameter-specific information 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 executes a CRM agent skill, runs an AI agent with a task description, and returns analysis. It uses specific verbs and provides examples of agents, distinguishing it from list_skills.
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 provides prerequisites (skill scope API key) and execution time but does not explicitly state when to use this tool versus alternatives like list_skills, nor does it mention exclusions.
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 must carry the disclosure burden. It indicates the tool produces risk scores and can accept either occupation or sector, but does not specify output format, behavior when both or neither params are provided, or any computational details. It adds some context but leaves room for ambiguity.
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, information-dense sentence that front-loads the core purpose and enumerates key output aspects. No filler or redundant text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (two optional params, no output schema), and the description provides the essential purpose and output dimensions. However, it leaves unstated whether the two parameters can be combined and what is returned if neither is supplied, a gap given the lack of annotations or 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?
The input schema already describes both parameters (NACE sector code/keyword, ISCO occupation code/keyword) with 100% coverage. The description only repeats this information ('NACE sector', 'ISCO occupation') without adding new semantic detail, so it meets the baseline but adds no extra 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 states a specific function ('AI automation risk scores') with clear scope ('by ISCO occupation or NACE sector'). It identifies the output dimensions (exposure, complementarity, risk level), distinguishing it from sibling salary tools like salary_overview which likely provide pay data.
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 when automation risk for an occupation or sector is needed, but provides no explicit when/when-not guidance or mention of alternative tools. No exclusions or prerequisites are stated, leaving the agent to infer suitability from 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly states a EUR 50,000 max limit, supports idempotency_key to prevent duplicates, and requires an 'order' scope API key. These are meaningful operational constraints that go beyond a simple action statement, though it does not describe the response format or side effects like payment or stock reservation.
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, each earning its place: core purpose, a mandatory input condition, and two critical constraints (limit and idempotency) plus auth requirement. It is front-loaded and free of redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is a mutation with no output schema and no annotations, so the description needs to convey essential context. It covers the most critical operational constraints (auth scope, max limit, idempotency, product identifier rule), and the schema covers required fields. However, the absence of any indication of what the response looks like (e.g., order ID) or potential error cases leaves a notable gap for an order-placing 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 description coverage is 90%, so the baseline is 3. The description adds a clear 'at least one' rule for product_id/product_slug, which the schema already implies through 'required if no product_slug' and vice versa. The idempotency_key mention is already in the schema. Thus the added value over the schema is marginal.
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 opens with a specific verb-resource pairing ('Place a product order') that clearly distinguishes it from sibling tools like create_quote (which likely creates a quote) and order_status (which checks status). Additional constraints like product identifier requirements reinforce its unique role.
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 does not explicitly mention alternative tools or when-not-to-use scenarios. It provides useful preconditions (must supply product_id or product_slug, requires order scope), but the 'when to use vs alternatives' guidance is only implied by the tool's name and sibling context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses that only in-stock products are returned and lists the output fields, which is useful. However, it does not mention pagination, result limits, or behavior when multiple filters are combined. For a search tool, this is acceptable but not rich.
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 long, front-loaded with the main purpose, and then elaborates with parameter usage. Every sentence earns its place with no redundancy 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?
The tool is a simple search with three optional parameters and no output schema. The description covers the returned fields, filtering options, and the special slug use case. It does not mention optionality or combination behavior, but for the tool's complexity it is reasonably 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?
The input schema already describes all three parameters (100% coverage), so the baseline is 3. The description adds value by giving concrete examples for type values and clarifying the slug's purpose for single-product lookup, plus describing search semantics. This exceeds the schema alone.
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 searches in-stock products in the catalog and lists the returned fields (name, price, type, availability). It distinguishes the action from sibling tools like get_prices or check_availability by focusing on catalog search, though it does not explicitly reference alternatives.
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 usage context: filter by type (with examples) or name search, and pass slug for single-product lookup. It does not name alternative tools or give explicit when-not-to-use guidance, but the instructions are sufficiently clear for common use cases.
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 the behavioral transparency burden. It discloses key behaviors: auto-calculation of totals with 21% VAT, returning a quote number and pricing breakdown, and requiring a 'quote' scope API key. These are meaningful beyond what the schema provides, though it could mention whether the operation is idempotent or if the draft state has limitations.
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 short, purposeful sentences: what it does, VAT calculation, return value, and authentication requirement. Every sentence adds value, with no repetitive or filler content, making it highly concise and well-structured.
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 5 parameters, no output schema, and no annotations, the description provides sufficient context: it states the action, key behavior (VAT), the return information (quote number and breakdown), and the required API scope. While it doesn't detail the exact structure of the pricing breakdown, for a creation tool this is generally adequate. Slight gap is the lack of clarification on quote vs. order in the description itself.
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 input schema has 100% coverage, documenting all 5 parameters clearly. The description adds context around line items and VAT calculation but does not elaborate on parameter formats or relationships beyond what the schema already states. Baseline 3 is appropriate when the schema handles the parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Create a draft quote' with a specific resource (quote) and verb (create), and adds context with '(piedavajums)' and line items. It effectively distinguishes itself from sibling tools like place_order, which handles orders, not quotes.
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 using this tool when a draft quote needs to be created, but it does not explicitly mention when to use it versus alternatives like place_order or get_prices. No exclusions or alternative tool suggestions are provided, leaving the usage guidance implied rather than explicit.
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 present, so the description must carry the full burden of behavioral disclosure. It discloses return contents and available agents, but does not explicitly mention read-only behavior or absence of side effects. For a list operation, this is adequate but not highly transparent.
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. The first states the exact purpose, and the second provides return value details and a list of available agents. No wasted words and information is front-loaded.
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 simplicity (0 params, no output schema), the description covers what it returns and lists all agent names. It could optionally connect to run_skill, but the description is sufficiently complete for the context.
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 tool has zero parameters and schema coverage is 100%, so the description does not need to explain parameters. Baseline 4 is appropriate for a no-parameter tool.
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 uses specific verb 'List' with resource 'available CRM agent skills', clearly stating the tool's function. It also lists the return contents (names, descriptions, pricing, example tasks) and enumerates the available agents, distinguishing it from sibling tools like run_skill.
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 when needing to discover available skills, but it does not explicitly state when to use this tool versus alternatives such as run_skill. No exclusions or conditional guidance are given.
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 provided, the description carries the full burden of behavioral disclosure. It states that the tool returns order details, product info, and delivery status, which reveals a read-only behavior. However, it doesn't mention potential errors (e.g., order not found), authentication needs, or side effects. This is adequate for a simple status check but 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?
The description is a single, clear sentence that packs in the purpose, method, and return information. It is front-loaded with the primary action ('Check order status') and contains no wasteful filler.
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 simplicity (one required parameter, no output schema), the description is reasonably complete. It explains what the tool does and what it returns, which is sufficient for an agent to use it correctly. A slightly higher score would require explicit guidance on when not to use it or error handling, but it is adequate for the 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?
The schema description for order_id ('Order ID (required)') covers 100% of the parameter semantics. The description's mention of 'by ID' aligns with the schema but adds no extra detail. Since schema coverage is high, the baseline of 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?
The description clearly states a specific verb ('Check'), resource ('order status'), and method ('by ID'). It distinguishes itself from sibling tools like place_order (which creates orders) and check_availability (which checks availability), so the purpose is unambiguous.
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 clearly implies when to use this tool: to check the status of an order by providing its ID. It doesn't explicitly exclude alternatives, but the context of the sibling tool list (e.g., place_order, check_availability) makes the appropriate use case evident. A score of 4 reflects clear context without explicit exclusions.
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 provided, the description carries the burden. The verb 'browse' implies a read-only operation, but the description does not explicitly state that it only lists bounties and does not perform claims, nor does it mention any side effects or required permissions. It provides minimal transparency beyond purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that states the action, resource, and key differentiator (USDC rewards) without any wasted words. It is easy to scan and fully serves its 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?
The tool is a simple browse operation with two optional parameters and no output schema. The description explains what the bounties are and that they can be claimed, which is sufficient for an agent to understand the tool's role. It could mention return format or default status behavior, but given the simplicity and well-documented schema, it is reasonably 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 coverage is 100% for both parameters (limit and status), so the description adds no additional parameter context. It mentions 'open' but does not clarify that status filtering is available or how limit works. Baseline 3 is appropriate since the schema already documents the parameters well.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'browse' with the resource 'agent bounties' and adds key details about USDC rewards and claimability. This clearly distinguishes it from sibling tools like soul_profile or search_products, none of which deal with bounties.
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 clearly conveys that this tool is for browsing available agent bounties, giving clear context. While it doesn't explicitly mention exclusions or alternatives, there is no overlapping sibling tool, so the usage context is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does reveal that results are limited to in-stock products, prices are in EUR, and price_per_kg is calculated. It could mention auth/error behavior, but for a simple read operation it is reasonably transparent.
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 with the main action upfront and no redundant wording. Every clause adds useful information about scope, currency, or computed output.
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 one-parameter read tool with no output schema, it explains the data returned, the currency, the in-stock filter, and the optional product slug. It doesn't enumerate response fields or pagination, but those are not essential given its simplicity.
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 input schema covers 100% of the single parameter with a clear description ('Specific product slug (optional — omit for all)'). The tool description merely restates the slug selectivity without adding new parameter-level meaning, so the baseline score of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Get') and resource ('current product prices in EUR') and clearly defines the result scope (all in-stock products or by slug). It also mentions 'calculated price_per_kg', which helps distinguish it from sibling tools like search_products or check_availability.
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 clearly states that the optional slug controls whether you get all in-stock products or a specific product, which gives solid context for how to invoke it. However, it does not explicitly say when not to use it or name alternative tools, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses the return format (quantities in bags/pallets per location) and the behavior when parameters are omitted (returns all in-stock products). While it implicitly indicates a read-only operation, it does not mention auth, rate limits, or error behavior, which would be expected for a perfect score.
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 delivered in two concise sentences, front-loaded with the primary action. It includes necessary details about parameter usage and return format without any unnecessary words or repetition.
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 absence of an output schema and annotations, the description covers the core behavior: what is checked, what is returned (quantities per location), and how to filter results. It may lack some finer details (e.g., whether zero-stock locations are included), but for a simple stock lookup tool, it is sufficiently 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?
The schema provides basic descriptions for product_id and product_slug, but the description adds crucial semantics: the parameters are alternatives, and omitting both returns all in-stock products. This goes beyond the schema's simple note that product_slug is an alternative to product_id.
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 checks product stock across warehouse locations and returns quantities per location, using a specific verb and resource. It is easily distinguished from sibling tools like search_products and get_prices, which are about searching and pricing rather than stock availability.
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 usage context: pass product_id or product_slug for a specific product, or omit both for all in-stock products. However, it does not explicitly mention when not to use this tool or name alternative tools, so it stops short of a full 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the formula, the factors involved, and the output format (kg, bags, pallets). It does not explicitly state that this is a read-only calculation, but the nature of the tool makes that implicit; the formula transparency adds value beyond a simple 'calculate'.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with dense, relevant information. The purpose is front-loaded, and the formula and output units are included without extraneous wording. 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?
For a calculation tool, the description covers the purpose, the formula, and the output units. The schema covers all parameter details, so no additional parameter documentation is needed. The tool is fully specified for an agent to invoke it correctly.
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?
Although the schema already describes each parameter (100% coverage), the description enriches them by mapping them to the formula: boiler_kw, insulation, desired_temp, and heating_months feed into the factors. This clarifies their role in the calculation beyond the schema's simple labels.
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 a specific action ('Calculate heating fuel needs') and the resource ('boiler specs'). It also names the output units (kg, bags, pallets), making the tool's purpose unambiguous and distinct from its unrelated siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool: when heating fuel needs must be calculated from boiler specifications. It does not explicitly state exclusions or alternative tools, but given the sibling set is unrelated to this calculation, the clear context is sufficient for an agent to decide.
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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