whatsapp-mcp

by rubickthemagus

Overview Schema Related Servers Score Discussions

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a distinct retrieval role: chats, chat-scoped messages, full-text search, context expansion, contacts, group membership, and database health. The potential overlaps among list_messages, search_messages, and get_chat_context are clearly separated by their descriptions.
Naming Consistency4/5
Most tools follow a snake_case verb_noun pattern like list_chats, list_messages, and search_messages. The lone 'stats' breaks the verb_noun convention, and get_group_members could have been list_group_members for stricter parallelism.
Tool Count5/5
With seven tools, the server is well-scoped and each tool addresses a distinct need for inspecting WhatsApp data. No tool feels redundant or extraneous.
Completeness4/5
The read/search workflow is well covered: chats, messages, search, context, contacts, group members, and health checks. The main gaps are direct single-message lookup and any send/manage operations, but for a read-only database inspection surface these are minor and workaroundable.
Average 3.4/5 across 7 of 7 tools scored. Lowest: 2.7/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit 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 MIT License.
This repository includes a README.md file.
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavior. 'List' implies a read-only operation, but the description does not mention pagination, ordering, whether group member expansion is included, or how the results are shaped.
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 short sentence with no filler. It is front-loaded and easy to parse, though the brevity comes at the cost of missing behavioral and parameter context.
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?
For a tool with no annotations, no output schema, and no parameter descriptions, the description is under-specified. An agent cannot tell the return format, how query filters contacts, or how group membership is resolved from the provided text alone.
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% and the description itself says nothing about limit or query. The names are suggestive but their exact semantics, such as fuzzy matching or required format, are left undocumented.
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?
States a clear action and resource: 'List known contacts and named group members.' Differentiates from list_chats/list_messages by resource, though get_group_members could overlap without an explicit comparison.
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 such as get_group_members, which also returns group members. The description does not specify context, filters, or exclusions.
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 burden of behavioral disclosure. It only says 'show' and 'surrounding,' which implies a read-only operation, but it does not explain ordering, default counts for before/after, or any other runtime behavior.
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 no filler. The main action and purpose are front-loaded, making it easy for an agent to quickly understand the tool's function.
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 simple, but the description leaves out parameter semantics and explicit usage guidance. It is minimally viable for invoking the tool correctly, but an agent would benefit from knowing that before/after control the number of messages on each side and that message_id is the anchor.
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 by explaining parameters. It does not mention message_id, before, or after explicitly. 'Surrounding' hints at the role of before/after, but the description adds little semantic value beyond the schema's property names.
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 ('Show') and resource ('messages surrounding one message') and states a clear purpose ('to expand a search hit'). This differentiates it well from siblings like search_messages and list_messages.
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 phrase 'to expand a search hit' implies the tool is used after a search, but it does not explicitly discuss when to use this tool versus alternatives or when not to use it. Usage context is present but only implied.
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 full burden. It clearly states a read operation ('List members') and promises admin flags, but does not disclose potential limitations such as requiring a group chat, auth/permissions, pagination, or error behavior.
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?
A single front-loaded sentence with no filler. Every word contributes: 'List' gives the action, 'members of a group chat' gives the scope, and 'with admin flags' adds return detail.
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?
For a simple one-parameter read tool, the description is minimally sufficient: it names the action and the object. However, with no annotations and no output schema, it lacks context about permissions, return shape, and how this tool fits with siblings, leaving some gaps for an agent to infer.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It does add meaning by identifying that the chat is a group chat, implying chat_id refers to a group chat ID, but it does not explicitly explain chat_id's role, format, or how to obtain it.
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 ('List') and resource ('members of a group chat') and adds a distinguishing detail ('with admin flags'). This clearly separates it from siblings like list_chats and list_messages, which target different resources.
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 about when to use this tool versus alternatives, nor any prerequisite such as first calling list_chats to obtain a chat_id. The usage context is only implied by the name and description.
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 behavior burden. It usefully discloses that this is a read operation, that results are ordered oldest first, and that after/before accept specific formats. It does not describe pagination behavior, from_me filtering semantics, or response contents.
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 tightly written sentences: the core function is front-loaded and the format note adds essential detail without bloat. Every sentence earns its place.
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?
Covers ordering and date syntax, which are the most important non-obvious traits. Gaps remain around pagination, filter behavior, and return shape, and since there is no output schema these omissions are more noticeable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description meaningfully documents the two ambiguous parameters, after and before, with concrete accepted formats. Limit, offset, and from_me are left to their titles and defaults, which are mostly self-explanatory.
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?
States the verb 'Read', the resource 'messages in one chat', and the ordering 'oldest first'. The phrase 'in one chat' distinguishes it from siblings like list_chats and search_messages, though it does not name them explicitly.
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?
Implies usage when you need messages from a specific chat, and provides date-format guidance, but gives no explicit when-to-use vs. alternatives such as search_messages or get_chat_context. There is no exclusion 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 the burden. It discloses ordering and the kind filter, but does not describe return shape, query semantics, or any auth/rate-limit considerations. This is a partially transparent read-list description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short and front-loaded, with the main action and sort order first and a single clarifying parameter note second. Every sentence earns its place without 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?
For a basic list operation this is serviceable, but with no output schema and no annotations it should state what a returned chat entry looks like and clarify the query parameter. These gaps prevent full end-to-end confidence.
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 should compensate. It only explains kind ('dm', 'group', or omit); limit, offset, and especially query are left undefined, even though query's semantics are not obvious from the parameter name alone.
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 and resource, 'List WhatsApp chats', and states the sort order, making it distinct from sibling tools such as list_messages and list_contacts. The kind line further clarifies scope by chat type.
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 that the tool is for retrieving an ordered chat list, but it never explicitly says when to prefer it over list_messages, search_messages, or get_chat_context. There are no exclusions or alternative-route conditions.
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 must disclose behavior itself, and it does reveal two non-obvious traits: results are sorted newest first and matching is literal substring rather than regex/fuzzy. However, it omits other behavioral details such as pagination, case sensitivity, and what happens with large result sets.
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 short sentences, no filler, with the most important behavioral facts (resource, ordering, matching mode) front-loaded. Every word contributes.
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?
For a five-parameter tool with no output schema and no annotations, the definition is incomplete. It does not explain the time/position parameters, result shape, or pagination, so an agent has to guess at valid usage for all optional parameters.
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%, and the description only clarifies the query parameter semantics ('matches a literal substring'). The after, before, limit, and chat_id parameters are left to name inference, with no date format, default behavior, or interaction explained.
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 action (Search) and resource (message text), and adds distinguishing detail: newest-first ordering and literal substring matching. This separates it from sibling tools like list_messages, which would not perform text search.
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 makes the intended use case clear — retrieving messages by a literal text substring — but it does not explicitly contrast this with list_messages or list_chats or state when not to use it. No misleading guidance is present.
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 explains what information the tool exposes, which is useful. However, it does not explicitly state that the operation is read-only, has no side effects, or what the exact return format will be, leaving some behavioral 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, efficiently front-loaded sentence. 'Database health' immediately frames the tool's purpose, followed by a compact list of concrete output elements. There is no repetition, filler, or redundant restating of the tool name.
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 zero-parameter, no-output-schema diagnostic tool, the description gives a good sense of what the agent will receive: source path, counts, date range, and sending status. It is sufficient for an agent to decide to call the tool and interpret its results at a high level. It could be slightly more explicit about the return shape, but the current description is functionally 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 tool has zero parameters and the schema coverage is 100%, so there are no parameter semantics for the description to clarify. Per the baseline for zero-parameter tools, the description does not need to compensate for missing parameter docs. The description focuses on the output, which is appropriate here.
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 a database health readout and enumerates the returned aspects: source path, counts, date range, and sending status. It distinguishes itself from sibling list/search tools by focusing on aggregate health stats rather than individual chats, messages, or contacts. However, it uses a noun phrase ('Database health') rather than an explicit verb like 'returns' or 'provides'.
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 the tool is for checking overall database health, which is enough for a zero-parameter diagnostic tool. It gives no explicit when-to-use direction or mention of alternatives, but the context makes the intended use reasonably clear. No exclusions or conditions are stated.
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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