Skip to main content
Glama

Get what the account has spent

get_usage
Read-onlyIdempotent

What the account SPENDS: one call, three answers, and they must never be mixed up. (1) THIS MONTH, a FORECAST: this_month_forecast gives total_minor for the month in progress, which is spent_minor (already debited) plus remaining_minor (what the active trackers and monitored corroborations will still run before month end, counted as real occurrences and recomputed from their configuration). Report it as a forecast, never as spend, and say the month. (2) PER MONTH, actual: by_month[] gives, for each of the last 12 months, month (YYYY-MM), spent_minor, and the same amount by project and by tracker. This is what the wallet was really debited. months_total says how many months have spend, so you can tell whether 12 covered everything. (3) OVER THE WINDOW, actual: total_spent_minor with by_project[] and by_tracker[] (biggest spender first) is a cumulative total over period (from and to, the first and last debit counted), never a monthly figure: quote the period alongside the amount. entries[] carries the ledger itself. Spend is broken down by COST LINE everywhere, in lines and by_category: survey (the checks themselves), ai_analyst (the supplement of the AI analysts grafted onto them), competitor_scan (a one-off scan of a basket of competitors, run from the app on the same check grid, which carries its own line and stays out of by_tracker), corroboration_check (monitored corroborations, one debit per check run) and other for a line the tool does not name yet, which stays visible rather than dropping out of a total. Amounts are in minor units of the wallet currency. Filter a single month with month (YYYY-MM), and the entries alone with type (in for top-ups, out for spend). Every tracker line carries tracker_id, ready for get_results or get_responses, and listed says whether that tracker is still in the account list. Each entry is stamped with created_at, the exact instant it was posted (RFC 3339, to the second, with the fraction the ledger recorded): an active account posts dozens of entries a day, so quote the time along with the date and let it order the entries of one day. On a check debit, on_demand says where the check came from: true for one asked for with survey_now, false for one the tracker cadence ran, null on an entry that is not a check debit. Separate the two when reporting spend, since one is a decision the account made and the other is the schedule running. Entries are paginated and page is CLAMPED: stop on page >= pages. get_credits gives what is left to spend.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNo
typeNoWhich entries to return: all (default), in (top-ups and adjustments), out (spend).
monthNoA single month, YYYY-MM. Omitted: every month the account has entries for.
per_pageNo

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already mark it read-only, idempotent, and non-destructive. The description adds substantial behavioral detail: pagination clamping, the on_demand semantics, cost-line breakdown, created_at precision, and the forecast/actual distinction. It fully discloses the operation's behavior without contradicting annotations.

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 long but highly structured, with numbered sections for the three answers, then cost lines, fields, and pagination. It front-loads the core purpose and each sentence carries informative weight given the tool's complexity. No redundancy or fluff.

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?

With no output schema, the description must explain return values, and it does so exhaustively: field meanings, forecast vs actual, cost-line breakdown, pagination limits, on_demand flags, and timestamp format. It also covers filtering options and points to get_credits for remaining spend. Nothing needed for correct invocation is omitted.

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?

Schema has 50% coverage (type and month have descriptions). The description enriches these by explaining type values ('in' for top-ups, 'out' for spend) and month format, and adds meaning to page (clamping) that the schema lacks. However, per_page is left undefined in text, relying on its numeric nature, so it doesn't fully compensate for all parameters.

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 states the tool returns account spend data in three distinct forms (this-month forecast, per-month actual, over-window actual) and clearly differentiates them, with a specific verb and resource. It also names a sibling (get_credits) to avoid confusion, making the purpose unambiguous.

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 instructs when to report forecast vs actual, warns against mixing them, explains the window is never monthly, and recommends separating on_demand sources. It also points to get_credits as the alternative for remaining spend, giving clear when-to-use and when-not-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4/5.0
Disambiguation4/5

Each tool maps to a distinct resource and action, and the descriptions go out of their way to separate near-neighbor concepts like surfaces vs corroborations and score series vs raw responses. A few related pairs (get_results/get_responses, get_credits/get_usage, create_surface/create_corroboration) could still be confused at a glance, so it is not a perfect 5.

Naming Consistency5/5

Tool names follow a highly consistent verb_noun snake_case pattern across all 67 tools, with clear families like create_, update_, get_, list_, archive_, restore_, and delete_. Minor quirks such as topup_credits as one word do not break the overall uniformity.

Tool Count1/5

67 tools is an extreme count for a single MCP server, even for a broad brand-monitoring domain. The surface is bloated with lifecycle variants per entity, and the sheer number makes the server hard to navigate and prompt against.

Completeness5/5

The server covers full lifecycles for projects, trackers, surfaces, corroborations, quests, logbook entries, keyword discoveries, competitor scans, link targets, sources, support, and billing. Archive/restore and soft-delete paths prevent dead ends, and nearly every obvious workflow has a corresponding tool.

Resources