Track cost, latency, and usage of every MCP tool call from any client (Claude, Cursor, Windsurf). Free 25K calls/month — open-source proxy, EU-hosted.
Estimate the USD cost of an MCP tool call BEFORE invoking it. Returns median + P90 + average cost from the org's last-30-day history for this exact (server, tool) combo. Use this to make spend-aware decisions in your agent — e.g. confirm with the user before invoking tools where the estimate exceeds your budget. Returns isUnknown=true with zero cost when no baseline exists yet.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | Optional model identifier. Falls back to the historical median across all models when omitted. | |
| toolName | Yes | Tool name within the server, e.g. "browser_navigate". | |
| serverName | Yes | MCP server name, e.g. "playwright" or "github". |
| Name | Required | Description |
|---|---|---|
| model | No | |
| toolName | Yes | |
| isUnknown | Yes | |
| avgCostUsd | No | |
| p90CostUsd | No | 90th percentile cost — worst-case reasonable estimate. |
| sampleSize | Yes | |
| serverName | Yes | |
| successRate | No | 0-1, fraction of historical calls that succeeded. |
| avgLatencyMs | No | |
| avgInputTokens | No | |
| avgOutputTokens | No | |
| estimatedCostUsd | Yes | Median cost from sample. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description transparently details the output: 'Returns median + P90 + average cost from the org's last-30-day history' and 'Returns isUnknown=true with zero cost when no baseline exists yet.' This adds significant value beyond the annotations (readOnlyHint, idempotentHint) by explaining the specific data returned and edge cases.
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 concise and front-loaded: three sentences that state purpose, usage, and edge case. Every sentence adds value with no unnecessary words.
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 the presence of an output schema (context signals indicate 'Has output schema: true'), the description adequately explains the return values and edge case. It covers all aspects needed for a pre-call estimation tool, and sibling tools are sufficiently differentiated.
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 description coverage is 100%, so the baseline is 3. The description reinforces the tool's context (exact server/tool combo) but does not add new semantics beyond the schema descriptions. The parameters are clearly documented in the schema.
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 clearly states the tool's purpose: 'Estimate the USD cost of an MCP tool call BEFORE invoking it.' It specifies the exact verb (estimate), resource (USD cost), and timing, distinguishing it from sibling tools like 'get_today_cost' or 'list_top_tools' which serve different purposes.
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 guidance: 'Use this to make spend-aware decisions in your agent — e.g. confirm with the user before invoking tools where the estimate exceeds your budget.' It also implies when not to use (when cost is irrelevant) and differentiates from siblings by focusing on per-call cost estimation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Drill into a single session by ID. Returns the session header plus every tool call within it (capped at 500 to bound payload size).
| Name | Required | Description | Default |
|---|---|---|---|
| session_id | Yes | Session ID from list_recent_sessions. Must belong to the caller's organization. |
| Name | Required | Description |
|---|---|---|
| id | Yes | |
| model | Yes | |
| startedAt | Yes | |
| toolCalls | Yes | |
| totalCostUsd | Yes | |
| toolCallCount | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint. Description adds behavioral context: returns header + tool calls, capped at 500. No contradiction.
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, zero waste, front-loaded with action verb. Every sentence adds necessary information.
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 1 parameter, no nested objects, and existing output schema, the description fully explains what the tool returns and its limits.
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 has 100% coverage with description. Description adds extra context: 'Must belong to the caller's organization.' Adds value beyond schema.
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?
Clearly states it drills into a single session by ID, returns header and tool calls (capped at 500). Distinguishes from sibling list_recent_sessions which lists sessions.
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?
Implicitly guides to use when needing session details. Mentions payload cap (500) as a usage constraint. Does not explicitly exclude other tools but context makes it clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Total tool-call cost and call count for the current day (UTC), for the organization behind the caller's API key. Returns a human-readable summary line plus raw numbers in a structured field.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| costUsd | Yes | Sum of estimated costs in USD. |
| callCount | Yes | Tool calls observed since 00:00 UTC. |
| errorCount | Yes | Tool calls that returned an error. |
| inputTokens | Yes | Sum of input tokens. |
| outputTokens | Yes | Sum of output tokens. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds value by detailing the output format (human-readable summary plus raw numbers in a structured field), which goes beyond annotations.
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 a single, clear, well-structured sentence that front-loads the key information. Every word adds value.
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?
With no parameters, read-only annotations, and an output schema, the description fully covers what the tool does and returns. No gaps.
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?
No parameters exist, so baseline is 4. The description is sufficient and does not need to add parameter info.
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 clearly states it returns today's total cost and call count, with specific scope (UTC, organization behind API key). It distinguishes from sibling tools like estimate_cost and get_usage_this_month.
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 implies usage for retrieving today's cost but does not explicitly state when to use it versus alternatives like estimate_cost or get_usage_this_month. No exclusions or conditions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Calls used this calendar month, the plan limit, percentage used, and a linear end-of-month projection based on the current daily average. Use to spot when an org will hit its cap.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| plan | Yes | Current plan tier. |
| callsLimit | Yes | Plan's monthly call cap. |
| percentUsed | Yes | callsThisMonth / callsLimit * 100. |
| callsThisMonth | Yes | Tool calls used since the billing cycle start. |
| organizationName | Yes | Name of the org owning this API key. |
| projectedEndOfMonth | Yes | Linear projection of total calls by end of UTC month. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the description needs to add context beyond that. It explains the tool returns a linear end-of-month projection based on daily average, which is valuable behavioral detail. No contradictions with annotations.
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: the first lists what the tool returns, the second provides the recommended use case. No unnecessary words, information is front-loaded. Excellent conciseness.
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?
The description fully explains what the tool does and its output. An output schema exists (though not shown), so return values are covered. The tool is simple with no parameters, and the description is complete enough for an agent to understand its usage without ambiguity.
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?
The tool has 0 parameters, and schema description coverage is 100%. Per the rubric, baseline is 4 for 0 params. The description doesn't need to explain parameters, but it does explain the output fields (usage, limit, percentage, projection), which adds value.
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 clearly states the tool returns monthly call usage data, plan limit, percentage used, and a linear projection. It specifies the resource (calls) and the action (get usage), making the purpose unmistakable. While it doesn't explicitly distinguish from siblings, the unique combination of usage data and projection sets it apart.
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 a clear use case: 'Use to spot when an org will hit its cap.' It does not explicitly state when not to use or list alternatives, but the use case is concrete and helpful. Sibling tools like 'get_today_cost' or 'estimate_cost' serve different purposes, so the guidance is adequate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent agent sessions for this organization, ordered by start time. Each row has the model, total cost, tool-call count, and duration in seconds.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum sessions to return. Defaults to 20. |
| Name | Required | Description |
|---|---|---|
| sessions | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, indicating safe, non-destructive behavior. The description adds value by specifying the output fields (model, cost, tool-call count, duration) and ordering by start time, which annotations do not cover.
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 key purpose, and contains no unnecessary words. Every sentence adds value.
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?
For a simple list tool with one parameter and an output schema (implied), the description is complete: it explains what is returned and the ordering. No further details are needed for basic usage, though it could mention that it only returns the most recent sessions.
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% with the single 'limit' parameter fully described (type, default, min, max). The description does not add additional parameter semantics beyond what the schema provides, so baseline score of 3 is appropriate.
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 clearly states the tool lists recent agent sessions for the organization, ordered by start time, and specifies the fields per row (model, cost, tool-call count, duration). This distinguishes it from sibling tools like get_session_details or get_today_cost.
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 implies usage for viewing recent sessions but does not explicitly state when to use this tool versus alternatives like get_session_details for a single session or get_today_cost for aggregate cost. No 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.
Top MCP tools by cost over the past N days. Answers "what is the most expensive thing my agents do" — useful before tightening prompts or swapping a server.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Lookback window in days. Defaults to 7. | |
| limit | No | Maximum entries to return. Defaults to 10. |
| Name | Required | Description |
|---|---|---|
| days | Yes | |
| tools | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, making safety clear. The description adds context about answering a specific question, but no behavioral traits beyond what annotations provide.
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: one states purpose clearly, the other adds context. No wasted words, front-loaded with key information.
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 output schema exists and annotations cover safety, description is complete for a simple list tool. Lacks explicit mention of ordering direction (descending by cost implied).
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 parameters are well-documented in schema. Description adds no additional meaning beyond schema, earning baseline 3.
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?
Clearly states it lists top MCP tools by cost over N days, answering a specific question. Distinguishes from sibling cost tools by focusing on ranking tools by cost.
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?
Provides a concrete use case ('useful before tightening prompts or swapping a server'), but does not explicitly mention when not to use or alternatives. Sibling names provide some context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run this WITHOUT an API key to see what MCPSpend output looks like. Returns a synthetic cost snapshot identical in shape to get_today_cost + list_top_tools + get_usage_this_month. Use this to preview the product before signing up.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| demo | Yes | Always true — this is sample data. |
| plan | Yes | |
| month | Yes | |
| today | Yes | |
| signUp | Yes | URL where the caller can create a real account. |
| topTools | Yes | |
| organizationName | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, and the description adds that it returns synthetic data identical in shape to three specific tools. No contradictions; adds valuable behavioral context.
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 key action ('Run this WITHOUT an API key'), zero wasted words. Every sentence adds value.
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?
With no parameters, existing output schema, and annotations covering safety, the description fully explains what the tool does, when to use it, and what it returns. Complete for its simplicity.
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?
No parameters exist, schema coverage is 100%, so the description need not add parameter info. Baseline 4 is appropriate.
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 states a specific verb ('Run this') and resource ('synthetic cost snapshot'), and clearly distinguishes it from siblings by specifying it mimics three other tools and requires no API key.
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?
Explicitly says to use without an API key and to preview before signing up, implying use when you lack authentication. Does not state when not to use (e.g., for real data), 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.
Claiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user or an account that owns the GitHub organization, then choose Claim with GitHub.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
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
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
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.
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Add one secure layer between your agents and this server.
Each tool has a distinct purpose: cost estimation, session details, daily cost, monthly usage, session listing, top tools, and demo. No two tools overlap in functionality.
All tool names follow a consistent verb_noun pattern (e.g., estimate_cost, get_session_details, list_top_tools). 'try_demo' also fits the pattern.
Seven tools is an appropriate number for a cost monitoring server, covering estimation, session details, daily/monthly usage, and top tools without being overwhelming.
The tool set covers core cost monitoring needs: estimation, session drill-down, daily/monthly totals, and top tools. Minor gaps like historical trends or budget management are missing but not critical for the stated purpose.