kazi-mcp

Server Quality Checklist

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Latest release: v0.1.3
Disambiguation5/5
Each tool addresses a distinct aspect of the Kenyan labor domain: matching, wages, skills gaps, informal sector registry, contracts, and rights. There is no overlap in purpose or functionality.
Naming Consistency5/5
All tool names follow a consistent lowercase_snake_case pattern using descriptive noun phrases (e.g., job_match, wage_benchmark, labor_rights_query). The naming is uniform and predictable.
Tool Count5/5
With 6 tools, the set is well-scoped for a labor/employment assistance server. Each tool covers a distinct need without redundancy or bloat.
Completeness4/5
The tools cover core labor workflows: matching, compensation, skills development, contracts, and legal rights. Minor gaps exist (e.g., no tool for dispute resolution or formal sector registration), but the surface is largely complete for the stated domain.
Average 3.7/5 across 6 of 6 tools scored. Lowest: 2.7/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 29 commits in the last 12 weeks
- Last stable release on July 18, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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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 present, so the description must carry the full burden of behavioral disclosure. It states the output ('returns ranked matches with wage ranges') but does not confirm that it is a read-only operation, explain any side effects, or note limitations. The 'DEMO' hint is left unexplained, leaving significant ambiguity about the tool's operational 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 compact and front-loaded, with the primary purpose in the first sentence. The second sentence adds return details. However, the appended 'DEMO' is cryptic and adds clutter without clarity, so it is not perfect.
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 tool description is incomplete for a 2-parameter tool with zero schema coverage. It omits the role of 'county', offers no context on Kenyan job categories or how matching is performed, and provides no relationship to sibling tools. The 'DEMO' marker raises questions without answers.
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 compensate. It implicitly references the 'skills' parameter ('Match worker skills'), but it fails to explain the 'county' parameter, including its format, purpose, or optionality. This leaves a critical part of the input under-specified.
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 function: 'Match worker skills to Kenyan job categories' with a specific verb and resource. It also mentions returning 'ranked matches with wage ranges,' which adds useful detail. However, it does not explicitly differentiate itself from sibling tools like wage_benchmark, so it loses one point.
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 alternatives. The description offers no context for selection, no exclusions, and no mention of appropriate use cases. The 'DEMO' tag is not elaborated as a usability caveat.
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?
Annotations are absent, so the description carries the full burden. It discloses only that the tool can 'register or look up', but gives no details about side effects, required permissions, data persistence, or the implications of the 'DEMO' marker. This is insufficient for an agent to anticipate 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 sentence that efficiently conveys the core purpose without unnecessary fluff. The 'DEMO' tag adds a small but relevant piece of context about its non-production nature. It is concise, though it sacrifices detail.
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 and four parameters, the description provides minimal context. It does not specify possible action values, required fields for registration vs lookup, or any constraints on inputs. The tool appears simple, but the description still leaves critical operational gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has four parameters with 0% description coverage, and the tool description does not mention or explain any of them. It fails to clarify what 'action' should be (e.g., 'register' vs 'lookup') or how 'name', 'trade', and 'county' are used, leaving the agent without semantic guidance.
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 registers or looks up informal sector workers, with specific examples (jua kali, boda rider, domestic worker). This clearly distinguishes it from sibling tools like job_match, wage_benchmark, etc., which address different labor-market 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 when dealing with informal sector workers (registering or looking them up) but does not explicitly specify when to use this tool over the siblings, nor does it provide any exclusions or alternative recommendations. Usage context is implied by the domain but lacks direct 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?
There are no annotations, so the description carries the burden. It discloses that this is a DEMO and should be verified against KNBS data, which is valuable. However, it does not explain behavior like data sources, return format, or edge cases, leaving many details undisclosed.
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 very concise, a single sentence plus a caveat, with no redundant information. It wastes no words and effectively front-loads the core 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?
Despite its brevity, the description covers the key purpose, target population, currency, and a reliability caveat. With a simple parameter set and an output schema present, this is sufficient for an agent to understand the tool's function and limitations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description does add meaning by noting experience levels (entry/mid/senior) and currency (KES). It does not explain the county parameter or the specific values for job_title, leaving some gaps but adding useful context 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 the tool provides a monthly wage benchmark for Kenyan jobs and specifies currency (KES) and levels (entry/mid/senior). It distinguishes from siblings like job_match or skills_gap_analysis by focusing on wage data, though it lacks an explicit verb like 'returns' or 'gets'.
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 purpose implies when to use it (for wage benchmarks in Kenya), and the DEMO warning gives context about reliability. However, there is no explicit guidance on when to use this tool versus alternatives, nor any 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?
No annotations are provided, so the description carries the full burden. It discloses a key limitation ('NOT legal advice') and the fact that it generates a template, but it does not describe other behavioral traits such as whether it validates inputs, whether the template is generic, or whether the output includes all legally required clauses. The disclaimer adds value but leaves gaps.
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 conveys purpose and scope, followed by a short necessary disclaimer. Every word earns its place; there is no redundancy or 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?
An output schema exists, so the description need not explain return values. The description covers the core purpose, contract types, and legal disclaimer, which is sufficient for a straightforward template generator. It does not address when to prefer this over sibling tools, but the tool's function is distinct enough that this is a minor gap.
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 has 0% description coverage, so the description must compensate. It does so by enumerating the allowed values for contract_type (permanent/casual/fixed_term) and adding legal jurisdiction context. The remaining parameters are self-explanatory from their names (employer_name, employee_name, job_title, monthly_gross_kes, start_date), so their meaning is recoverable.
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 ('Generate') and names the exact resource ('Kenya Employment Act 2007 contract template') with a parenthetical list of contract types (permanent/casual/fixed_term). This clearly distinguishes it from sibling tools like job_match or wage_benchmark, which are analysis/comparison tools rather than document generators.
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 provides clear context: use this tool when you need a Kenyan employment contract template under the Employment Act 2007, with options for permanent, casual, or fixed-term roles. It doesn't explicitly name alternatives or exclusions, but the legal disclaimer 'NOT legal advice' adds a caution about appropriate use.
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 transparency burden. It discloses that results are a DEMO summary and not legal advice, which is a key behavioral limitation. It also implies read-only via 'Query.' It does not add further details like output format or rate limits, but for a simple query tool this is adequate and adds value beyond the schema.
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 with no filler. Every part adds value: the verb, the resource (Kenya Employment Act 2007), the parameter guidance (by topic with examples), and the disclaimer.
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 (one parameter) and has an output schema, so the description need not elaborate on return values. The description covers purpose, parameter hints, and a key limitation. It feels complete for a demo-level query tool, though it could optionally mention which specific topics are covered more precisely.
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 no description or enums for the 'topic' parameter, so the description compensates by listing example topics: 'maternity, overtime, termination, etc.' This gives the agent a clear sense of acceptable values, though it does not define exhaustive options or formatting.
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: 'Query Kenya Employment Act 2007 rights by topic' with specific verb and resource. It distinguishes itself from sibling tools by focusing on legal rights topics (maternity, overtime, termination), which is unique among the listed tools.
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 querying labor rights by topic and includes an important caveat: 'DEMO summary — not legal advice.' However, it does not explicitly contrast with alternative tools or state when not to use it beyond the legal disclaimer, so guidance is partially 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?
With no annotations provided, the description carries the full burden of disclosure. It states what the tool returns ('missing skills + Kenya training pathways') and implies a read-only analysis, but it does not explicitly state side effects, limitations, or data sources. This 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 two sentences, front-loaded with the core purpose, and contains no filler. Every word 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 tool's simplicity (2 params), the presence of an output schema, and clear sibling differentiation, the description is almost complete. It lacks explicit usage guidance relative to siblings, but the purpose and returns are well covered. A small gap is the absence of any mention of limitations or prerequisites.
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 has 0% description coverage, so the description must compensate. It explicitly names the two parameters ('current skills' and 'target job') and explains their role in the analysis, adding meaning beyond the raw schema. It does not provide format examples, but the context is clear.
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 ('Identify') and resource ('skills gap between current skills and target job'), and it distinguishes from siblings by focusing on gap analysis rather than matching, wages, or legal queries. The output ('missing skills + Kenya training pathways') further clarifies its purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool: when you have current skills and a target job and need to identify gaps and training pathways. It does not explicitly mention alternatives or exclusions, but the purpose is sufficiently distinct from sibling tools.
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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