FlashLeads MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool has a clearly distinct purpose: get_web_harvest_leads retrieves collected lead data, get_web_harvest_status checks agent status and counts, and run_web_harvest initiates or updates the harvesting process. There is no overlap in functionality, making tool selection straightforward for an agent.
Naming Consistency5/5All tool names follow a consistent verb_noun pattern with snake_case (e.g., get_web_harvest_leads, get_web_harvest_status, run_web_harvest). The naming is predictable and readable, using clear verbs like 'get' and 'run' paired with descriptive nouns.
Tool Count3/5With only 3 tools, the set feels thin for a web harvesting domain, as it lacks operations like updating or deleting leads, managing agent configurations, or filtering/searching through collected data. While the core functions are present, the scope is limited, which might require workarounds for more complex tasks.
Completeness3/5The tools cover the basic lifecycle of web harvesting: initiating (run_web_harvest), monitoring (get_web_harvest_status), and retrieving results (get_web_harvest_leads). However, there are notable gaps, such as no ability to modify or delete leads, control agent settings, or perform advanced queries on the collected data, which could limit agent effectiveness in extended workflows.
Average 3.4/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues 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.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add
glama.jsonto the root of your repository:{ "$schema": "https://glama.ai/mcp/schemas/server.json", "maintainers": [ "your-github-username" ] }Then . Browse examples.
Add related servers to improve discoverability.
How to sync the server with GitHub?
Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.
To manually sync the server, click the "Sync Server" button in the MCP server admin interface.
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 full burden for behavioral disclosure. It states what information is returned but doesn't describe important behavioral aspects: whether this is a read-only operation (implied by 'get'), whether it requires authentication, rate limits, pagination behavior (beyond the limit parameter), error conditions, or what happens when no leads exist. For a tool with no annotation coverage, this leaves significant gaps in understanding how the tool behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with two sentences that each serve a clear purpose: the first states the core function, the second details the return information. There's no wasted text, and it's front-loaded with the main purpose. However, it could be slightly more structured by explicitly separating purpose from return details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is minimally adequate. It covers what the tool does and what information it returns, but lacks important context: no output format details, no error handling information, no guidance on when to use versus siblings, and incomplete behavioral transparency. For a data retrieval tool with no output schema, the description should do more to explain what 'detailed information' actually means in practice.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does 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 (status with enum values and limit with default). The description adds no parameter-specific information beyond what's in the schema - it doesn't explain parameter interactions, provide examples, or clarify semantics. With complete schema coverage, the baseline is 3 even without additional param info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get the leads collected by your web harvest agent' specifies the verb (get) and resource (leads from web harvest agent). It distinguishes from sibling 'get_web_harvest_status' (which likely returns status info) and 'run_web_harvest' (which initiates harvesting), but doesn't explicitly differentiate them. The description is specific about what information is returned (company names, websites, contact details, social media profiles).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 when to use it instead of 'get_web_harvest_status' or 'run_web_harvest', nor does it provide context about prerequisites (e.g., whether a harvest must be completed first). The only implied usage is retrieving leads, but no explicit alternatives 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.
- 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 describes key actions ('search Google', 'collect leads', 'creates or updates agent') and output data, but lacks details on permissions, rate limits, error handling, or whether the operation is idempotent. The mention of agent management adds some context, but behavioral traits are not fully transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence and efficiently details the output in the second. It avoids redundancy and uses clear language, though it could be slightly more concise by integrating the output details into the initial statement without sacrificing clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a tool that performs web harvesting and agent management, with no annotations and no output schema, the description is moderately complete. It covers the purpose and output data but lacks details on behavioral aspects, error cases, and how the agent creation/update works. This leaves gaps for an AI agent to fully understand the tool's operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema fully documents the three parameters. The description does not add any parameter-specific semantics beyond what the schema provides, such as examples or constraints not in the schema. The baseline score of 3 is appropriate as the schema handles the parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does 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 ('search Google', 'collect company website leads', 'creates or updates your web harvest agent') and resources ('leads with company names, websites, emails, phone numbers, and social media profiles'). It distinguishes from sibling tools like 'get_web_harvest_leads' and 'get_web_harvest_status' by emphasizing the active harvesting and agent management functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for lead generation from Google searches but does not explicitly state when to use this tool versus alternatives like the sibling tools. No guidance is provided on prerequisites, exclusions, or specific scenarios where this tool is preferred over others, leaving the agent to infer context from the tool name and description 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. It describes what information is returned (status and counts) but doesn't disclose behavioral traits like whether this requires authentication, has rate limits, or how frequently the status updates. The description is functional but lacks operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that efficiently communicates the tool's purpose and key outputs. Every word earns its place with no redundancy or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter status-check tool with no output schema, the description adequately covers what information is returned. However, without annotations or output schema, it doesn't specify the format of the status response or potential error conditions, leaving some operational gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and schema description coverage is 100% (empty schema). The description appropriately doesn't discuss parameters, maintaining focus on the tool's purpose. Baseline for zero parameters is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific verb ('check') and resource ('web harvest agent'), specifying what information is retrieved (running status and lead counts). It distinguishes from sibling tools by focusing on status monitoring rather than getting leads or initiating harvests.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context (monitoring ongoing harvest operations) but doesn't explicitly state when to use this vs. alternatives like 'get_web_harvest_leads' or 'run_web_harvest'. No explicit exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
GitHub Badge
Glama performs regular codebase and documentation scans to:
- Confirm that the MCP server is working as expected.
- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
Our badge communicates server capabilities, safety, and installation instructions.
Card Badge
Copy to your README.md:
Score Badge
Copy to your README.md:
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/MOHAMAD-ZUBI/mcp-server'
If you have feedback or need assistance with the MCP directory API, please join our Discord server