RegexForge
Server Details
Deterministic regex synthesis from labeled examples. Zero LLM, proof matrix, backtracking audit.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- walkojas-boop/regexforge
- GitHub Stars
- 0
- Server Listing
- RegexForge
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.5/5 across 1 of 1 tools scored.
With only one tool, there is no possibility of confusion between tools. The tool's purpose is clearly defined and distinct.
The single tool name 'regexforge_synth' follows a clear service_action pattern, making it predictable and self-explanatory. With only one tool, there are no inconsistencies.
The server has only one tool, which feels thin for a general-purpose toolkit but is acceptable for a narrowly focused service. The tool is comprehensive in its functionality, but having only one tool may leave agents wanting more basic utilities like regex validation or explanation.
The tool covers the entire workflow of regex synthesis from examples, including a test matrix and backtracking risk analysis. There are no obvious missing operations within the stated domain of generating battle-tested regexes.
Available Tools
1 toolregexforge_synthAInspect
Synthesize a battle-tested regex from labeled examples. Input: optional natural-language description + 20 examples (10 positive, 10 negative, or any mix of ≥4). Output: regex + flags + test matrix proving every example is handled correctly + backtracking-risk analysis. Deterministic; no LLM at serve time.
| Name | Required | Description | Default |
|---|---|---|---|
| examples | Yes | ||
| description | No | Optional natural-language description, used only for tie-breaking. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosing behavior. It states determinism, no-LLM-at-serve-time, and the guaranteed test matrix with backtracking-risk analysis. It does not cover failure modes or edge cases, but the provided behavioral traits are sufficient for usage understanding.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core purpose, then covering inputs, outputs, and behavioral guarantees. Every sentence contributes value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description enumerates all output components (regex, flags, test matrix, backtracking analysis). It also covers input constraints and core behavioral characteristics, making it complete for an agent to decide on and invoke the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 50%; the description compensates by detailing that the description parameter is optional and used for tie-breaking, and by explaining example mix constraints (10 positive/10 negative or ≥4 total). This adds meaning beyond the schema's simple field definitions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'Synthesize a battle-tested regex from labeled examples.' It clearly states the tool's function and distinguishes it from generic regex tooling by emphasizing the synthesis from examples, plus its deterministic nature.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit input requirements (optional description + 20 examples, or any mix ≥4) and output expectations (regex, flags, test matrix, backtracking analysis). It also gives behavioral guidance ('Deterministic; no LLM at serve time'). However, it does not explicitly contrast with alternatives since no sibling tools are present.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!
Related MCP Servers
- AlicenseAqualityCmaintenanceProvides tools to test regex patterns for correctness, performance (ReDoS), and memory usage, and suggests safe rewrites. Enables LLMs to iterate on regex generation with verifiable feedback.Last updated9MIT
- AlicenseAqualityCmaintenanceEnables LLMs to systematically develop and validate regex patterns by defining test cases with expected matches, testing patterns against them, and iteratively refining until all requirements are satisfied.Last updated46MIT
- AlicenseAqualityBmaintenanceDeterministic AI safety policy engine with Z3 formal verification. Write, verify, simulate, and enforce machine-verifiable safety constraints for AI agents. Completely outside the LLM.Last updated615Apache 2.0
- AlicenseAqualityCmaintenanceStatic worst-case token-budget analysis for LLM-agent workflows using AST analysis to identify certifiable, default-dependent, non-certifiable, and runaway units, with optional signed budget certificates.Last updated457MIT
Your Connectors
Sign in to create a connector for this server.