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Glama

Algenta MCP Server

by io.github.thyn-ai

responses

Run the unified Algenta utility response surface: deterministic tokenization/embeddings, or (for provider-backed chat models) a chat response with optional function/tool calling. input accepts a plain string, a list of independent strings (each processed as its own single-turn request), or a typed OpenResponses-style input array (items shaped {type: message|function_call|function_call_output, ...}) processed as ONE multi-turn conversation. previous_response_id continues a prior typed-array conversation -- state is held in the Algenta server process's memory only, so it does not survive a process restart or a different worker/replica.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYes
modelNotext.tokenizer
toolsNo
dimensionsNo
tool_choiceNo
parallel_tool_callsNo
previous_response_idNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed5 schema fields changed
    • changedInput schema / properties / input / oneOf
      Previous value: -[
-  {
-    "type": "string"
-  },
-  {
-    "items": {
-      "type": "string"
-    },
-    "type": "array"
-  }
-]New value: +[
+  {
+    "type": "string"
+  },
+  {
+    "items": {
+      "type": "string"
+    },
+    "type": "array"
+  },
+  {
+    "items": {
+      "properties": {
+        "type": {
+          "enum": [
+            "message",
+            "function_call",
+            "function_call_output"
+          ],
+          "type": "string"
+        }
+      },
+      "required": [
+        "type"
+      ],
+      "type": "object"
+    },
+    "type": "array"
+  }
+]
    • addedInput schema / properties / parallel_tool_calls
      Added value: +{
+  "type": "boolean"
+}
    • addedInput schema / properties / previous_response_id
      Added value: +{
+  "type": "string"
+}
    • addedInput schema / properties / tool_choice
      Added value: +{
+  "oneOf": [
+    {
+      "enum": [
+        "auto",
+        "none",
+        "required"
+      ],
+      "type": "string"
+    },
+    {
+      "properties": {
+        "function": {
+          "properties": {
+            "name": {
+              "type": "string"
+            }
+          },
+          "required": [
+            "name"
+          ],
+          "type": "object"
+        },
+        "type": {
+          "enum": [
+            "function"
+          ],
+          "type": "string"
+        }
+      },
+      "required": [
+        "type",
+        "function"
+      ],
+      "type": "object"
+    }
+  ]
+}
    • addedInput schema / properties / tools
      Added value: +{
+  "items": {
+    "properties": {
+      "function": {
+        "properties": {
+          "description": {
+            "type": "string"
+          },
+          "name": {
+            "type": "string"
+          },
+          "parameters": {
+            "type": "object"
+          }
+        },
+        "required": [
+          "name"
+        ],
+        "type": "object"
+      },
+      "type": {
+        "enum": [
+          "function"
+        ],
+        "type": "string"
+      }
+    },
+    "required": [
+      "type",
+      "function"
+    ],
+    "type": "object"
+  },
+  "type": "array"
+}
  2. First observed

TDQS

A3.6/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It meaningfully reveals that string lists are processed as independent single-turn requests, typed arrays form one multi-turn conversation, and previous_response_id state is in-memory only and does not survive a restart or worker/replica change. This is genuinely useful beyond the schema.

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

Conciseness4/5

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

The description is three dense sentences, front-loaded with the core purpose and then progressively specifying input modes and state caveats. There is no filler, though the long parenthetical about typed array item shapes could be tightened without loss.

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?

For a 7-parameter unified endpoint with no output schema and no annotations, the description covers core input semantics and state persistence details. It omits the return value shape, how tool calling interacts with the different modes, and when provider-backed chat vs deterministic tokenization/embeddings is selected beyond the default model name. An agent would still need more information to call it fully correctly.

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

Parameters3/5

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

Schema description coverage is 0%, so the description must compensate. It adds real semantics for `input` (three accepted forms and their processing behavior) and `previous_response_id` (memory-only state). However, it leaves `model`, `dimensions`, `tools`, `tool_choice`, and `parallel_tool_calls` unexplained, forcing the agent to infer their meaning from names and schema defaults.

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?

The description opens with a specific verb ('Run') and names the resource ('unified Algenta utility response surface'), then enumerates the distinct modes: deterministic tokenization/embeddings and provider-backed chat with tool calling. It is clear about scope, but it does not explicitly differentiate this from sibling tools like chat_completions, tokenize, or embeddings.

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

Usage Guidelines3/5

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

The description provides useful contextual guidance on input shapes—plain string, list of independent strings, or typed array as one multi-turn conversation—which implies when each form is appropriate. However, it never states when to prefer this unified tool over alternatives such as chat_completions, tokenize, or embeddings, nor gives any exclusions.

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

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TDQS

C2.7/5.0
Disambiguation4/5

Most tools target distinct resources or actions, but there is some overlap (e.g., run_repository_fix vs run_repository_pipeline vs simulate_repository) that could cause confusion. Overall, descriptions help differentiate.

Naming Consistency3/5

Tool names are primarily snake_case with a verb_noun pattern, but there are inconsistencies (e.g., single-word verbs like 'simulate', 'tokenize', and mixed prefixes like 'preview_', 'product_'). The pattern is readable but not uniform.

Tool Count1/5

With 140 tools, the server is extremely over-scoped for typical MCP usage. This overwhelms agents and suggests poor separation of concerns, likely violating the principle of minimal tool surfaces.

Completeness4/5

The tool set covers a wide range of functionalities including data onboarding, simulation, decisions, repository management, and admin operations. Minor gaps exist (e.g., no update_agent_run), but core workflows are well-supported.

Resources