Budgetary: estimate token spend
Server Details
Pre-flight, probabilistic token-spend estimate (range, scenario, confidence) for a coding task.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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 4.2/5 across 1 of 1 tools scored.
Only one tool exists, so there is no possibility of confusion or overlap between tools.
With a single tool, naming consistency is not a concern; the name 'estimate' is clear and fits its purpose.
A single tool for a very narrow purpose (token spend estimation) is borderline thin but acceptable for a specialized server.
The single tool covers its stated purpose of providing probabilistic estimates, but lacks any complementary tool for actual usage tracking or task management.
Available Tools
1 toolestimateAInspect
Return a pre-flight, probabilistic token-spend estimate (range + scenario + confidence) for a coding task before you run it. Estimates only — it never reports your actual token usage.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | Optional model identifier, e.g. claude-opus-4-7. | |
| query | Yes | The coding task or prompt you're about to run. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It states it is a 'pre-flight, probabilistic token-spend estimate' and explicitly notes it never reports actual usage, disclosing key behavioral traits. Missing details like side effects or authorization needs, but as an estimate-only tool, 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the core purpose and qualification. Every sentence adds value with no waste. Excellent structure.
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?
Given no output schema, the description explains the return type (range, scenario, confidence). The tool is simple with two params, and the description covers all essential aspects. Could mention scenario granularity or confidence range formatting, but adequate.
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 100%, so baseline is 3. The description adds context for the 'query' parameter as a 'coding task you're about to run,' which slightly enhances understanding beyond the schema's generic description. No additional config beyond that.
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 explicitly states the tool returns a 'probabilistic token-spend estimate (range + scenario + confidence) for a coding task before you run it.' It clearly identifies the verb (return), resource (token-spend estimate), and scope (pre-flight), and distinguishes itself from actual token usage reporting.
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 tells when to use it: 'before you run' a coding task. It also clarifies what it does not do: 'never reports your actual token usage.' This provides clear context, though no explicit alternatives are mentioned.
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!