Skip to main content
Glama
Log-LogN

langfuse-mcp-java

upsert_llm_connection

upsert_llm_connection
Destructive

Manage LLM provider connections by creating new ones or updating existing configurations with required API keys for OpenAI, Anthropic, Azure, or Google services.

Instructions

Creates or updates an LLM provider connection (upserted by provider name). If a connection for the given provider already exists, it is updated. provider and secretKey are required. provider examples: openai, anthropic, azure, google.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
providerYesProvider name, e.g. openai, anthropic, azure, google. Required.
secretKeyYesAPI secret key for the provider. Required.
adapterYesAdapter name.
Behavior3/5

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

Clarifies upsert semantics beyond what annotations provide (explaining the update-when-exists logic). However, it fails to reconcile the idempotentHint=false annotation with upsert expectations, and doesn't explain the destructive impact of updates despite destructiveHint=true.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Four sentences with some redundancy (explicitly stating parameters are required when the schema already enforces this). The structure progresses logically from action to conditional behavior to requirements, though the requirement sentence is redundant.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers basic upsert mechanics but omits security context for secretKey handling (relevant given destructiveHint=true), doesn't explain connection usage within the system, and ignores the output implications (no output schema exists).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema coverage, baseline is 3, but the description incorrectly states only provider and secretKey are required, omitting the adapter parameter which is also required per the schema. It also fails to explain what the adapter parameter does beyond the schema's minimal description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states the tool creates or updates an LLM provider connection and explains the upsert keying mechanism (by provider name). However, the title merely repeats the function name in snake_case rather than providing human-readable context.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explains the conditional behavior (creates new if absent, updates if exists) but provides no guidance on when to use this versus list_llm_connections or other sibling tools, nor preconditions like required configuration states.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Log-LogN/langfuse-mcp-java'

If you have feedback or need assistance with the MCP directory API, please join our Discord server