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Glama
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Server Details

Diagnose AI agent failures & translate ambiguous human input into clear intent using RPCS-1.

Status
Healthy
Last Tested
Transport
Streamable HTTP
URL
Repository
travisbergen2/rpcs1-sdk
GitHub Stars
0
Server Listing
RPCS-1 Agent Tuner & Translation Bridge

Glama MCP Gateway

Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.

MCP client
Glama
MCP server

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.

100% free. Your data is private.
Tool DescriptionsA

Average 4.1/5 across 7 of 7 tools scored. Lowest: 3.4/5.

Server CoherenceA
Disambiguation4/5

Most tools have distinct purposes: calibrate_profile for profiles, interpret for ambiguity detection, normalize for text cleanup, etc. However, interpret and prepare_prompt both deal with ambiguity, potentially causing confusion.

Naming Consistency3/5

The naming pattern is inconsistent: some tools use verb_noun (calibrate_profile, prepare_prompt) while others are single verbs (interpret, normalize, rewrite). This mix could confuse an agent.

Tool Count5/5

With 7 tools, the set is well-scoped for the server's purpose of profile-driven communication tuning. Each tool serves a clear function without excess.

Completeness4/5

The tool surface covers core workflows: profile calibration, ambiguity handling, text normalization, prompt/reply processing, style rewriting, and configuration diagnostics. Minor gaps like profile storage are handled externally.

Discussions

travisbergen2's avatar
travisbergen2Jun 14, 2026

RPCS1 is stateless and does not store, list, or update recommendations. Identical inputs produce identical outputs; clients should persist results when history is needed.

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