BitScale MCP Server

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.1.8
Disambiguation5/5
Each tool has a distinct purpose with no overlap: grid listing, details, curl generation, execution, status polling, workspace info, and key rotation are clearly separated.
Naming Consistency5/5
All tool names use consistent verb_noun snake_case pattern (e.g., get_grid_details, list_grids, rotate_api_key), making predictable and easy to use.
Tool Count5/5
7 tools are well-scoped for a grid enrichment service, covering essential operations without bloat or missing core functionality.
Completeness4/5
Covers the main workflow: discovery, inspection, execution, status, and admin tasks. Minor gaps like canceling runs or managing data sources are absent but not critical.
Average 4.7/5 across 7 of 7 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
- Last stable release on March 18, 2026
- 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.json to 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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It explains the tool returns a curl command and structured API contract, including how required inputs are derived from column dependencies. It does not mention authentication needs or side effects, but as a read-only operation, this is sufficient.
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 well-structured with a recommended workflow, bullet points for parameters, and a clear list of returned fields. It is concise, front-loaded, and every sentence 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?
For a simple tool with 2 parameters and an existing output schema, the description is thorough. It details all return fields and the workflow. Minor omission: no mention of error handling or edge cases, but overall complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description compensates fully. It explains grid_id as a UUID from the grid URL or list_grids, and output_columns as an optional comma-separated list with an example, adding significant meaning 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's purpose: 'Get a ready-to-use curl command and structured API contract for running a specific Grid.' The verb 'Get' and the resource 'curl command and API contract' are specific and distinguish it from sibling tools like 'run_grid' and 'list_grids'.
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 explicitly recommends calling this tool before 'run_grid' to discover input fields, providing a clear workflow. It implies when to use (before running) and offers guidance on obtaining the grid_id. However, it does not explicitly state when not to use or exclude alternatives.
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?
The description lists the returned fields in detail, implying a read-only operation. No annotations are provided, so the description carries the full burden. It is transparent but could mention any side effects or permissions.
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 efficiently structured with a brief purpose statement followed by a bulleted list of returned fields. Every sentence is informative with no redundancy.
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?
Given zero parameters and detailed output description, the description covers all necessary context for an agent to invoke the tool and interpret results. No gaps are apparent.
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?
Zero parameters are defined, and the description justifies why no parameters are needed (automatic workspace identification). This adds meaning beyond the empty 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 retrieves details about the current BitScale workspace, including specific categories like plan info, credit balances, and member counts. It is distinct from sibling tools that deal with grids, runs, or API keys.
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 explains that no parameters are needed and the workspace is identified automatically from the API key. While it does not explicitly contrast with sibling tools, the context makes its use case clear.
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 full burden. It discloses that the tool returns a paginated list with column details, explains the column UUID usage for run_grid, and mentions case-insensitive substring matching for search. No behavioral contradictions.
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 well-structured with Args and Returns sections, but could be slightly more concise. However, it is efficient and front-loaded with key information.
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?
Given the tool has 3 parameters and an output schema, the description is thorough. It covers return format, pagination, column details, and usage hints for related tools. No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description fully compensates. It clearly explains each parameter: search (optional, case-insensitive substring match), page (1-based, default 1), limit (default 20, max 100), with examples.
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 lists all grids with column definitions, defines 'grids' as spreadsheet-like tables, and distinguishes from siblings by noting it is used to discover grids before calling get_grid_details or run_grid.
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 explains when to use (discover available grids before running them) and how to use the results (grid id for other tools), but does not explicitly state when not to use or mention alternatives beyond the sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses irreversibility, immediate key stoppage, and need to update integrations and restart server. With no annotations provided, description fully covers 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?
Concise: two sentences plus return format. Front-loaded with purpose, then warning, then output. Every sentence adds value.
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?
Complete for a parameterless tool. Output schema shown via example, no missing context. Complex behavior (irreversible mutation) fully disclosed.
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?
Zero parameters in schema; description adds no param info, but baseline is 4 for no parameters. Schema coverage is 100% by default.
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 clearly states it generates a new API key and invalidates the current one, with specific verb+resource. Distinct from all sibling tools which handle grid or workspace details.
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?
Explicit warning about immediate invalidation and required updates (integrations, restart). No alternatives to exclude since no other tool rotates keys, but context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It reveals that the operation is read-only (get metadata) and discloses important behavioral nuance: the distinction between column UUIDs (for 'output_columns') and human-readable labels (for 'inputs') in 'run_grid'. This prevents misuse.
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 well-structured with a clear header, usage note, args, returns, and a critical note section. However, the 'Args' section redundantly repeats the parameter definition already in the schema. Minor verbosity prevents a perfect score.
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?
Given the complexity of grid metadata and its relationship to 'run_grid', the description covers all essential aspects: purpose, usage context, parameter details, return structure, and crucial caveat about UUIDs vs labels. The presence of an output schema makes the returns section sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter 'grid_id' has no schema description (0% coverage). The description fully compensates by explaining its format (UUID), where to find it (URL or list_grids), and its purpose, adding high value 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 verb 'Get' and resource 'full metadata for a specific Grid'. It distinguishes itself from siblings like 'list_grids' and 'run_grid' by focusing on schema inspection. The explicit list of included metadata (column definitions, settings, sources) leaves no ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use this to inspect a grid's schema before running it' and explains why – to understand input labels and output column UUIDs for 'run_grid'. It also tells where to find the 'grid_id' parameter, providing clear when-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavior: sync mode waits up to 120 seconds, async returns immediately, and the return format includes status transitions. It explains that appending a row triggers enrichments and returns outputs. No contradictions with annotations (none provided).
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 well-structured with sections, bullet points, and a labeled Args list. It is slightly verbose but every sentence adds value. The key differentiation of input vs output key formats is highlighted prominently. Could be tightened slightly, but overall efficient.
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?
Given the tool's complexity (5 params including nested objects, output schema, async pattern), the description is nearly exhaustive. It covers prerequisites (get_grid_details), input discovery (BitScale app panel), execution modes, return formats, and polling instructions. It also references sibling tools appropriately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage, so the description carries full burden. It adds rich meaning to all 5 parameters: explains grid_id origin, inputs key format (human-readable LABELS vs UUIDs) with examples, mode options with behavior, output_columns as optional UUIDs from get_grid_details, and source_id as optional override. It also describes the return object structure.
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 purpose: 'Run a BitScale Grid by appending a new row with the given inputs and triggering all column enrichments.' It uses a specific verb ('Run') and resource ('BitScale Grid'), and distinguishes from siblings like get_grid_details (schema discovery) and get_run_status (polling).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit when-to-use guidance: 'Before calling this, use get_grid_details to understand the grid schema.' It explains sync vs async mode trade-offs, and mentions alternative tools (get_run_status for polling, get_grid_details for schema). It also clarifies when not to use this tool (e.g., need to discover input labels first).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description fully discloses behavior: polling is required, mentions rate limit implications, and details the return structure including possible status values and output format when completed. This covers all key 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 concise (8 lines), with a clear argument section and return description. Every sentence adds value: purpose, usage, polling guidance, and output format. No wasted words.
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?
The description is fully complete for a single-parameter polling tool. It covers the entire workflow: when to use, how to poll, what to expect in return, and how to interpret status. Output schema is implicitly described.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter request_id is described as 'The request_id UUID returned by run_grid', adding crucial context beyond the schema's minimal title. With 0% schema description coverage, this fully compensates.
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 'Check' and resource 'status of a previously triggered Grid run', clearly distinguishing it from sibling tools like run_grid (which triggers) and get_grid_details (which gets grid details).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use this tool ('after run_grid returns a request_id'), provides polling advice (every 2-5 seconds), warns about rate limit, and defines termination conditions ('completed' or 'failed'). No alternatives need exclusion.
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:

[![bitscale-mcp MCP server](https://glama.ai/mcp/servers/featherflow/bitscale-mcp/badges/card.svg)](https://glama.ai/mcp/servers/featherflow/bitscale-mcp)

Score Badge

Copy to your README.md:

[![bitscale-mcp MCP server](https://glama.ai/mcp/servers/featherflow/bitscale-mcp/badges/score.svg)](https://glama.ai/mcp/servers/featherflow/bitscale-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

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/featherflow/bitscale-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server