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toolforte

by io.github.Toinedotcom

List remembered keys

memory_list
Read-onlyIdempotent

List the keys you have stored, newest first, optionally filtered by prefix. Use this to discover what a previous session left behind before deciding what to read. Returns keys and sizes, not values. Requires a free API key (Authorization: Bearer tf_...) so entries stay private to you: https://toolforte.com/developers

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of keys to return, 1 to 200 (default 200)
prefixNoOnly list keys starting with this prefix

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
keysNo
countNoNumber of items
resultNoThe result, when it is not an object

Schema Changelog

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

  1. Changed2 schema fields changed
    • addedInput schema / properties / limit / description
      Added value: +"Maximum number of keys to return, 1 to 200 (default 200)"
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "$schema": "https://json-schema.org/draft/2020-12/schema",
+  "additionalProperties": {},
+  "properties": {
+    "count": {
+      "description": "Number of items",
+      "type": "number"
+    },
+    "keys": {
+      "items": {},
+      "type": "array"
+    },
+    "result": {
+      "description": "The result, when it is not an object"
+    }
+  },
+  "type": "object"
+}
  2. First observed

TDQS

A4.5/5.0
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, and the description adds genuinely new behavioral facts: newest-first ordering, retrieval of keys and sizes rather than values, and the API-key requirement for private storage. No contradiction with 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?

Every sentence earns its place: action and sort order, intended use, return payload shape, and authentication requirement. The most decision-relevant facts are front-loaded.

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?

For a read-only, optional-parameter list tool with an output schema, the description covers discovery use case, ordering, filtering, response content, and authentication. Nothing necessary for correct invocation is missing.

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 100%, so the schema fully documents limit and prefix. The description independently notes prefix filtering but adds no parameter syntax or format detail beyond the schema, so the baseline score applies.

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 opens with a specific verb and resource — 'List the keys you have stored' — and adds ordering, optional filtering, and an explicit note that it returns keys and sizes, not values. This clearly distinguishes it from memory_get, memory_set, and memory_delete.

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

Usage Guidelines4/5

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

'Use this to discover what a previous session left behind before deciding what to read' gives a clear when-to-use context and implies memory_get is for reading values. It does not explicitly name sibling alternatives or state when not to use it, so it stops just short of full routing guidance.

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

A3.8/5.0
Disambiguation5/5

Every tool targets a distinct resource or action, and the detailed descriptions clearly separate near neighbors like generate_test_bsn versus generate_brp_test_data, read_page versus url_screenshot versus url_to_pdf, and image_compress/convert/resize. Even with 40 tools, there is no real boundary-blurring overlap.

Naming Consistency3/5

All names are snake_case and readable, but the set mixes conventions: verb_noun (generate_*, validate_*), noun_verb (pdf_merge, image_resize), conversion-style names (csv_to_json, html_to_pdf), and bare nouns (base64, qr_code_png). The groups are recognizable, but there is no single predictable pattern.

Tool Count2/5

Forty tools is an oversized surface for an agent to consider on every call, well above the point where tool selection cost starts to hurt. The broad purpose explains the count, but many one-off utilities could be grouped or exposed selectively.

Completeness3/5

The server covers many domains—encoding, Dutch test data, image/PDF handling, memory, and workflows—but several categories are partial: there are no reverse conversions like json_to_csv or html_to_markdown, no PDF text extraction, and no workflow create/update/delete tools. Agents can work around some gaps, but notable operations are missing.

Resources