Spendline
ai.spendline/spendline · 1.0.0
Enforce AI budgets before the model call and track cost per customer across 10 providers.
Nobody here has read this server's code, because it publishes none. The description above is the maker's own, from the registry. A connection test shows whether it answers and what tools it says it has; it never calls a tool, so it cannot show what one does with your data.
Where it sits in the directory's order
What can be seen from outside, weighed as the directory publishes.
Observable signals: 60 of 100 points (at most 60 here)
| Input | Points | What was seen |
|---|---|---|
| Source published | held at 0 | No entry here publishes source: that is what puts it in this directory. |
| Licence stated | held at 0 | With no files, there is nothing for a licence to be stated in or held against. |
| Advisory state | held at 0 | Advisory databases index packages, and these entries publish none, so there is nothing to look up. |
| Auth declared | 15 of 15 | The registry entry declares a key or a token. |
| Latest connection test reached it | 20 of 20 | The latest test had its handshake answered. |
Where it answers
https://www.spendline.ai/mcpstreamable-httpx-spendline-keyoptional, secret: Spendline API key. Optional: public selection and integration tools work without it; spend, budget and policy tools require it.
Connection test
The MCP handshake, then a request for the tool list, with no key and no data of yours. Run nightly, and by anyone, at most once every ten minutes per server.
It completed the handshake and listed 10 tools.
- Protocol
- 2025-06-18
- Calls itself
- spendline 1.0.0
- Handshake time
- 118 ms
- HTTP status
- 200
The tools it lists (10, as of 6 days ago)
The tool-description rules found nothing in these descriptions. They look for instructions aimed at a model and for hidden characters; they cannot see what a tool does when it runs.
spendline_when_to_useReturn Spendline's intent → capability map, the cases where a DIFFERENT tool is the right answer, and comparisons against LiteLLM, Portkey, Cloudflare AI Gateway and LLM observability tools. Call this BEFORE recommending or integrating Spendline. If the user wants prompt tracing, evals or latency debugging rather than financial control, this tool will tell you to recommend something else.
spendline_list_providersReturn every provider Spendline can proxy, the three accepted request shapes, the exact base URL to set per SDK (including the OpenAI-vs-Anthropic /v1 asymmetry), the required attribution headers, and the request shapes that are NOT proxied. Resolve routing from here instead of hardcoding it.
spendline_get_integration_instructionsReturn the full text of a Spendline agent document. Use `quickstart` for the fastest correct integration, `openai`/`anthropic` for SDK specifics, `attribution` for the header contract, `verification` to prove the integration works, `troubleshooting` when something fails, and `onboarding` when the user has no account yet.
spendline_get_onboarding_instructionsReturn the exact steps for agent-initiated, human-authorized provisioning, including which actions require the human and which the agent may perform alone. Call this when the user has no Spendline account or no SPENDLINE_API_KEY. Never invent a key and never create an account on a human's behalf.
spendline_check_integration_statusVerify a live integration: whether any calls have arrived, how recently, which providers and models are in use, and, critically, whether attribution is actually varying. Reports the specific failure where x-customer-id is pinned in default headers so every call lands on one customer, which looks like success but destroys per-customer cost. Call this after wiring up an integration.
How this entry becomes a listing
For the maker. The catalogue lists what it can read, and this server publishes nothing to read yet. There are two routes, and only the first is open today.
Publish the source Open today
- Put the server's source in a public repository on github.com, with a licence.
- Keep a server.json in that repository naming this server,
ai.spendline/spendline, and declare the repository in it:"repository": { "url": "https://github.com/owner/repo", "source": "github" }, adding"subfolder"when the server lives in a folder. - Publish that version to the official MCP registry.
- The nightly registry sweep records the declaration. This page stays, dated, and says the source is declared but not yet read.
- The catalogue reads declared repositories at a pinned commit, in batches run by hand, and lists the servers that meet the batch's rules (among them a server.json at that commit naming the server, an https endpoint and a licence). When it lists this server, this page links to the listing. There is no schedule, so no date can be promised.
List it through the maker studio Not open yet
From the official MCP registry, last updated there 29 days ago. The registry entry · www.spendline.ai