WireMCP

by 0xKoda

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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation3/5
There is significant overlap between tools, particularly among those that capture live traffic (capture_packets, check_threats, get_conversations, get_summary_stats), which could cause confusion as they all involve live capture but serve slightly different analysis purposes. However, the descriptions help differentiate their specific outputs (e.g., raw packet data vs. threat checking vs. conversation statistics), preventing complete ambiguity.
Naming Consistency4/5
Tool names follow a consistent verb_noun pattern (e.g., analyze_pcap, capture_packets, check_ip_threats), with all using snake_case and clear action-object phrasing. The only minor deviation is 'check_threats' lacking a specific object like 'ip' compared to 'check_ip_threats', but overall naming is predictable and readable.
Tool Count5/5
With 7 tools, the count is well-scoped for a network analysis server, covering key areas like packet analysis, threat detection, and credential extraction. Each tool appears to serve a distinct function within the domain, avoiding bloat while providing comprehensive coverage for typical tasks.
Completeness4/5
The tool set covers core network analysis workflows, including packet capture, threat checking, credential extraction, and statistical analysis, with both live and file-based options. A minor gap is the lack of tools for modifying or filtering traffic, but agents can likely work around this for most analysis tasks, making the surface reasonably complete.
Average 3/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 7 community issues answered or closed in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the output format (JSON for LLM analysis) but doesn't cover critical aspects like whether this is a read-only operation, potential performance impacts, error handling, or what 'general packet data' entails. For a tool with no annotations, this is insufficient.
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, efficient sentence that front-loads the core purpose. It avoids unnecessary details, but could be slightly more structured by explicitly separating input and output aspects. Overall, it's concise and well-sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'general packet data' includes, potential limitations, or how the JSON is structured for LLM analysis. For a tool with no structured support, more context is needed to guide effective use.
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% description coverage, with 'pcapPath' clearly documented as the path to the PCAP file. The description adds no additional parameter semantics beyond what the schema provides, such as file format requirements or path validation. With high schema coverage, the baseline score of 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 states the tool's purpose: 'Analyze a PCAP file and provide general packet data as JSON for LLM analysis.' It specifies the verb (analyze), resource (PCAP file), and output format (JSON for LLM analysis). However, it doesn't explicitly differentiate from sibling tools like 'capture_packets' or 'extract_credentials,' which prevents a score of 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a PCAP file), exclusions, or comparisons to siblings like 'check_ip_threats' or 'get_summary_stats.' This lack of context leaves the agent without clear usage instructions.
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 full burden for behavioral disclosure. It states the action ('Check') but doesn't describe what happens during execution (e.g., network call, rate limits, authentication needs, response format, or error handling). This leaves significant gaps for a tool that likely involves external API calls.
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, efficient sentence that directly states the tool's function without unnecessary words. It's appropriately sized and front-loaded with the core action, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of threat-checking (likely involving external APIs), no annotations, and no output schema, the description is insufficient. It doesn't explain what the tool returns (e.g., threat score, details, or just a boolean), error conditions, or operational constraints, leaving critical context missing.
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%, with the parameter 'ip' well-documented in the schema (including pattern validation). The description adds no additional parameter semantics beyond implying it's for threat checking, so it meets the baseline of 3 where the schema does the heavy lifting.
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 verb ('Check') and resource ('IP address') with specific context ('against URLhaus blacklist for IOCs'), making the purpose unambiguous. However, it doesn't explicitly distinguish this tool from sibling 'check_threats', which appears to be a similar threat-checking tool, preventing a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'check_threats' or other siblings. It mentions the specific blacklist (URLhaus) but doesn't explain why one would choose this over other threat-checking methods or tools, leaving usage context unclear.
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 full burden for behavioral disclosure. It mentions capturing traffic and checking against a blacklist but fails to describe critical traits like required permissions, network access needs, potential system impact, rate limits, or what the output looks like. For a tool that interacts with network traffic and external threat databases, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with a single sentence that directly states the tool's purpose. Every word earns its place, and there's no unnecessary information or repetition. It's appropriately sized for a simple tool with good schema coverage.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (network traffic capture and threat checking) and lack of annotations and output schema, the description is insufficient. It doesn't explain what happens during capture, how results are returned, error conditions, or security implications. The agent would need to guess about important operational details.
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 fully documents both parameters (duration and interface). The description doesn't add any parameter-specific information beyond what's in the schema, such as explaining why these parameters matter or providing usage examples. Baseline 3 is appropriate when the schema does the heavy lifting.
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 purpose with specific verbs ('capture live traffic' and 'check IPs') and resource ('URLhaus blacklist'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'capture_packets' or 'check_ip_threats', which appear to have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'capture_packets' or 'check_ip_threats'. It lacks context about prerequisites, appropriate scenarios, or exclusions, leaving the agent to infer usage from the purpose 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 provided, the description carries full burden for behavioral disclosure. It states what the tool does but doesn't describe important behavioral aspects: whether this is a read-only operation, what format the extracted credentials are returned in, whether the PCAP file is modified, error handling, or performance characteristics. The mention of 'for LLM analysis' hints at output format but is insufficiently specific.
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, efficient sentence that communicates the core purpose without unnecessary words. It could be slightly more structured by separating the credential types from the purpose statement, but overall it's appropriately sized and front-loaded with the main action.
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 that processes security-sensitive data (PCAP files with credentials) with no annotations and no output schema, the description is incomplete. It doesn't address critical context: what format the extracted credentials are returned in, whether this is a safe read operation, what happens with the PCAP file, or error scenarios. The mention of 'for LLM analysis' provides some output context but is too vague.
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 the single parameter 'pcapPath' with its description. The tool description doesn't add any additional parameter information beyond what's in the schema. This meets the baseline expectation when schema coverage is high.
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 action ('extract potential credentials') and target resource ('from a PCAP file'), with specific credential types listed (HTTP Basic Auth, FTP, Telnet). However, it doesn't explicitly differentiate from sibling tools like 'analyze_pcap' or 'get_conversations', which might also process PCAP files for different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'analyze_pcap' or 'check_threats'. It mentions 'for LLM analysis', but this is a vague purpose statement rather than practical usage instructions. There are no explicit when/when-not scenarios or prerequisites 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'capture live traffic' and 'provide statistics', but doesn't specify what 'capture' entails (e.g., whether it's passive monitoring, requires admin privileges, affects network performance, or stores data). It also omits details like rate limits, output format, or error conditions. For a tool that interacts with network interfaces, this leaves significant gaps in understanding its 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, efficient sentence that front-loads the core purpose. It avoids unnecessary words and gets straight to the point. However, it could be slightly more structured by separating the capture and statistics aspects, but this is minor.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of network traffic capture and the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'conversation statistics' include (e.g., counts, protocols, bytes), how results are returned, or any behavioral constraints. For a tool with no structured output and potential system-level interactions, more context is needed to guide effective use.
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%, with both parameters ('duration' and 'interface') well-documented in the schema. The description doesn't add any meaning beyond the schema—it doesn't explain how these parameters affect the capture or statistics, or provide usage examples. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
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 purpose with specific verbs ('capture live traffic' and 'provide TCP/UDP conversation statistics') and identifies the resource (network traffic). However, it doesn't explicitly differentiate from sibling tools like 'capture_packets' or 'analyze_pcap', which appear related to network analysis. The mention of 'for LLM analysis' adds specificity but doesn't clarify sibling distinctions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'capture_packets' (which might capture raw packets) or 'analyze_pcap' (which might analyze saved files), leaving the agent to guess based on names alone. There are no explicit when/when-not instructions or prerequisites 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool captures live traffic and provides statistics, implying a read-only operation, but doesn't mention potential side effects (e.g., network performance impact), permissions required, rate limits, or what happens if the interface is unavailable. For a tool that interacts with live network traffic, this is a significant gap in 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, efficient sentence that front-loads the core functionality ('capture live traffic and provide protocol hierarchy statistics') and includes the purpose ('for LLM analysis'). There is zero waste or redundancy, making it highly concise and well-structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of live network capture and analysis, the description is incomplete. No annotations exist to cover behavioral aspects, and there's no output schema to explain return values (e.g., statistics format). The description lacks details on error conditions, performance implications, or how results are formatted, leaving gaps for the agent to operate effectively in this context.
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%, with both parameters ('duration' and 'interface') well-documented in the schema. The description adds no additional parameter semantics beyond what the schema provides, such as valid interface examples beyond 'en0' or typical duration ranges. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but doesn't need to.
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 purpose with specific verbs ('capture live traffic' and 'provide protocol hierarchy statistics') and identifies the resource (network traffic). It distinguishes from siblings like 'analyze_pcap' (which likely analyzes existing files) and 'capture_packets' (which may capture without analysis), though it doesn't explicitly name these alternatives. The purpose is specific but could be more 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?
The description provides no guidance on when to use this tool versus alternatives like 'capture_packets' or 'analyze_pcap'. It mentions 'for LLM analysis' which hints at a context, but doesn't specify prerequisites, exclusions, or comparative scenarios. Without explicit when/when-not instructions, the agent lacks clear usage direction.
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 full burden but provides minimal behavioral information. It mentions 'capture live traffic' which implies a potentially privileged operation requiring network access, but doesn't disclose permission requirements, whether it's destructive, rate limits, or what happens during capture. The description adds some context about output format but misses critical behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that communicates the core purpose and output format without any wasted words. It's appropriately sized and front-loaded with the main action.
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 network capture tool with no annotations and no output schema, the description is insufficient. It doesn't explain what 'raw packet data as JSON' actually contains, how much data might be returned, whether there are size limits, or what permissions are required. The description leaves too many open questions for a tool that performs privileged network operations.
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 fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions 'capture live traffic' which aligns with the parameters but provides no additional syntax, format, or usage details for the parameters.
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 specific action ('capture live traffic'), the resource ('raw packet data'), and the output format ('as JSON for LLM analysis'). It distinguishes from siblings like analyze_pcap (which analyzes existing files) or check_ip_threats (which focuses on threat detection rather than raw capture).
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 live traffic capture and LLM analysis, but doesn't explicitly state when to use this tool versus alternatives like analyze_pcap (for existing files) or get_summary_stats (for aggregated data). It provides some context but lacks explicit guidance on tool selection.
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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