gildea 0.2.3

Python client and MCP server for the Gildea AI market intelligence API

Gildea

Python client and MCP server for the Gildea AI market intelligence API.

Gildea tracks 500+ expert sources on AI, decomposes every signal into verified reasoning chains (thesis, arguments, claims, evidence), and serves it through a REST API. This package gives you a Python client and an MCP server so AI assistants can use the data directly.

Install

# Python client only
pip install gildea

# With MCP server
pip install gildea[mcp]

Quick Start

from gildea_sdk import Gildea

client = Gildea(api_key="gld_your_key_here")

# Search verified text units
results = client.search(query="data center infrastructure spending")
for hit in results["data"]:
    print(hit["unit"]["text"])
    print(f"  Source: {hit['citation']['signal_title']} ({hit['citation']['registrable_domain']})")

# Get full signal decomposition with evidence
signal = client.signals.get("signal_id", include="evidence")

# Entity intelligence with trend analytics
entity = client.entities.get("NVIDIA")
print(f"{entity['display_name']}: {entity['direction']}, {entity['scale']} scale, {entity['notability']} notability")

# Cross-source consensus mapping
similar = client.search(similar_to="unit_id")

# Embed arbitrary text into Gildea's vector space
vector = client.embed("Data center capex is accelerating.")["embedding"]

Local similarity search

Pair include="embeddings" on signal detail with client.embed() to compute cosine similarity between user content and verified Gildea units, fully client-side:

import numpy as np
from gildea_sdk import Gildea

client = Gildea(api_key="gld_your_key_here")

# 1. Embed user content
user_vec = np.array(client.embed("I think infrastructure spending will slow in H2.")["embedding"])

# 2. Fetch signal with per-unit embeddings
signal = client.signals.get("sig_01JABCDEF123456789", include="embeddings")

# 3. Find semantically related text units
def iter_units(decomp):
    for arg in decomp.get("arguments", []):
        yield from arg.get("sentences", []) + arg.get("claims", [])
    yield from decomp.get("claims", [])
    if "thesis" in decomp:
        yield from decomp["thesis"].get("sentences", [])
    if "summary" in decomp:
        yield from decomp["summary"].get("sentences", [])

for u in iter_units(signal["decomposition"]):
    if "embedding" in u:
        sim = float(np.dot(user_vec, np.array(u["embedding"])))  # both unit-normalized
        if sim > 0.7:
            print(f"[{sim:.3f}] {u['unit']['text']}")

Embeddings are 768-dim BAAI/bge-base-en-v1.5 vectors. Both endpoints return vectors in the same space. See docs.gildea.ai/concepts/embeddings for details.

MCP Server

Use Gildea as a tool in Claude, ChatGPT, Cursor, VS Code, or any MCP-compatible client.

# Run directly
gildea-mcp

# Or via uvx (no install needed)
uvx --from gildea[mcp] gildea-mcp

Set your API key:

export GILDEA_API_KEY=gld_your_key_here

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "gildea": {
      "command": "uvx",
      "args": ["--from", "gildea[mcp]", "gildea-mcp"],
      "env": {
        "GILDEA_API_KEY": "gld_your_key_here"
      }
    }
  }
}

Available MCP Tools

Tool	Description
search_text_units	Semantic search across verified text units, or vector similarity via similar_to
list_signals	Browse signals by entity, theme, date, content type
get_signal_detail	Full decomposition: thesis, arguments, claims, evidence
get_entity_profile	Entity trend analytics, co-occurrence, theme distribution
list_entities	Discover entities by trend direction, notability, scale
get_themes	Theme overview across value chain and market force axes
get_theme_detail	Single theme trend analytics and cross-theme relationships

API Key

Get your API key at gildea.ai. Free tier includes 5 requests/minute and 200 requests/month.

Documentation

Full API docs at docs.gildea.ai.

License

MIT